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julien-ur/TakeTheBroom | UserStudy/.ipynb_checkpoints/evaluation_pre_study-checkpoint.ipynb | 1 | 20160 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre-Processing"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import glob"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"def load_csvs(subject_id):\n",
" temp_events = \"study_data/s%d_events%s.csv\"\n",
" temp_gamestatus = \"study_data/s%d_gamestatus%s.csv\"\n",
" \n",
" events = []\n",
" gamestatus = []\n",
" split_counter = 0\n",
" split = \"\"\n",
"\n",
" while True:\n",
" if split_counter > 0:\n",
" split = \"_%d\" % split_counter\n",
" try:\n",
" ev_tmp = pd.read_csv(temp_events % (subject_id, split), sep=';')\n",
" gs_tmp = pd.read_csv(temp_gamestatus % (subject_id, split), sep=';')\n",
" ev_tmp['SubjectId'] = subject_id\n",
" gs_tmp['SubjectId'] = subject_id\n",
" ev_tmp['SplitCount'] = split_counter\n",
" gs_tmp['SplitCount'] = split_counter\n",
" events.append(ev_tmp)\n",
" gamestatus.append(gs_tmp)\n",
" \n",
" except FileNotFoundError:\n",
" break\n",
" \n",
" split_counter += 1\n",
" \n",
" return pd.concat(events).reset_index(), pd.concat(gamestatus).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def move_custom_data(re, rg):\n",
" for index, re_row in re.copy().iterrows():\n",
" if re_row.TaskType == 'Custom':\n",
" rg_split_idx = rg.index[rg.SplitCount == re_row.SplitCount]\n",
" cam_cont_pos = re_row.TaskPos.strip('() ').split(',')\n",
" print(cam_cont_pos)\n",
" rg.loc[rg_split_idx, 'CamContXPos'] = cam_cont_pos[0]\n",
" rg.loc[rg_split_idx, 'CamContYPos'] = cam_cont_pos[1]\n",
" rg.loc[rg_split_idx, 'CamContZPos'] = cam_cont_pos[2]\n",
" re.drop(index, inplace=True)\n",
" continue\n",
" \n",
" return re, rg"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def add_rc_count_to_events(re, rg):\n",
" re['RepeatCount'] = pd.Series(np.array(np.zeros(re.size), dtype='uint8'))\n",
" \n",
" last_time = 0\n",
" last_round_type = re.iloc[0].RoundType\n",
" last_split_count = 0\n",
" repeat_count = 0\n",
" start_index = 0\n",
" \n",
" for index, re_row in re.iterrows():\n",
" if (re_row.SplitCount != last_split_count) | (re_row.RoundType != last_round_type) | (index == re.index[-1]):\n",
" re.loc[start_index : index, 'RepeatCount'] = repeat_count\n",
" \n",
" sc_rt_rg_mask = (rg.SplitCount == last_split_count) & (rg.RoundType == last_round_type)\n",
" sc_rt_re_mask = (re.SplitCount == last_split_count) & (re.RoundType == last_round_type)\n",
"\n",
" last_rc_in_rg = rg.loc[(rg.Timestamp <= last_time) & sc_rt_rg_mask].RepeatCount.max()\n",
" # print (last_round_type, \"sc\", last_split_count, \"time\", last_time, \"rc\", repeat_count, \"lrc\", last_rc_in_rg)\n",
"\n",
" if repeat_count < last_rc_in_rg:\n",
" for rc in range(repeat_count+1, last_rc_in_rg+1):\n",
" rc_round = rg.loc[(rg.RepeatCount == rc) & sc_rt_rg_mask]\n",
" rc_start = rc_round.iloc[0].Timestamp\n",
" rc_end = rc_round.iloc[-1].Timestamp\n",
" \n",
" # print (last_round_type, repeat_count, last_rc_in_rg, rc_start, rc_end)\n",
" re.loc[(re.Timestamp >= rc_start) & (re.Timestamp <= rc_end) & sc_rt_re_mask, 'RepeatCount'] = rc\n",
" \n",
" start_index = index\n",
" repeat_count = 0\n",
" last_round_type = re_row.RoundType\n",
" last_split_count = re_row.SplitCount\n",
" \n",
" elif (re_row.Timestamp < last_time):\n",
" re.loc[start_index : index, 'RepeatCount'] = repeat_count\n",
" start_index = index\n",
" repeat_count += 1\n",
" \n",
" last_time = re_row.Timestamp\n",
" \n",
" return re, rg"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def create_rounds(re, rg):\n",
" round_columns = ['SubjectId', 'Round', 'RoundType', 'Trial', 'RepeatCount', 'SplitCount', 'ValidTrial', 'StartTime', 'EndTime', 'Duration']\n",
" round_data = []\n",
" \n",
" re[\"Trial\"] = pd.Series(np.array(np.zeros(re.size), dtype='uint8'))\n",
" re[\"ValidTrial\"] = pd.Series([], dtype=bool)\n",
" rg[\"Trial\"] = pd.Series(np.array(np.empty(re.size), dtype='uint8'))\n",
" rg[\"ValidTrial\"] = pd.Series([], dtype=bool)\n",
" \n",
" # iterate over all available rounds\n",
" for r in rg.Round.unique():\n",
" trialNum = 0\n",
" rg_round = rg.loc[rg.Round == r]\n",
" round_type = rg_round.iloc[0].RoundType\n",
" \n",
" re_round_idx = re.index[(re.RoundType == round_type)]\n",
" re.loc[re_round_idx, 'RoundType'] = round_type\n",
" \n",
" for sc in rg_round.SplitCount.unique():\n",
" rg_split = rg_round.loc[rg_round.SplitCount == sc]\n",
" \n",
" for rc in rg_split.RepeatCount.unique():\n",
" rg_trial = rg_split.loc[rg_split.RepeatCount == rc]\n",
" \n",
" trial_start = rg_trial.iloc[0].Timestamp\n",
" trial_end = rg_trial.iloc[-1].Timestamp\n",
" trial_dur = trial_end - trial_start\n",
" \n",
" rg_trial_idx = rg.index[(rg.Round == r) & (rg.SplitCount == sc) & (rg.RepeatCount == rc)]\n",
" re_trial_idx = re.index[(re.RoundType == round_type) & (re.SplitCount == sc) & (re.RepeatCount == rc)]\n",
" \n",
" if trial_dur <= 15:\n",
" rg.drop(rg_trial_idx, inplace=True)\n",
" re.drop(re_trial_idx, inplace=True)\n",
" continue\n",
" \n",
" re_trial = re.loc[re_trial_idx]\n",
" rings = re_trial.loc[(re_trial.TaskType == 'Ring') & (re_trial.TaskStatus != 'visible')].EventId.unique().size\n",
" povs = re_trial.loc[(re_trial.TaskType == 'POV') & (re_trial.TaskStatus != 'visible')].EventId.unique().size\n",
" \n",
" valid_trial = False\n",
"\n",
" if (round_type == 'Training_Ring_Only') & (rings == 20):\n",
" valid_trial = True\n",
" elif (round_type != 'Training_Ring_Only') & (povs == 9):\n",
" valid_trial = True\n",
" \n",
" round_data.append({\n",
" 'SubjectId': rg_round.iloc[0].SubjectId, 'Round': r, 'RoundType': round_type,\n",
" 'Trial': trialNum, 'RepeatCount': rc, 'SplitCount': sc,\n",
" 'ValidTrial': valid_trial, 'Duration': trial_dur})\n",
" \n",
" rg.loc[rg_trial_idx, 'Trial'] = trialNum\n",
" rg.loc[rg_trial_idx, 'ValidTrial'] = valid_trial\n",
" \n",
" re.loc[re_trial_idx, 'Trial'] = trialNum\n",
" re.loc[re_trial_idx, 'ValidTrial'] = valid_trial\n",
" \n",
" trialNum += 1\n",
" \n",
" return pd.DataFrame(data=round_data, columns=round_columns), re, rg"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def process_events(re, rg):\n",
" re[\"EndTime\"] = pd.Series([], dtype=float)\n",
" re[\"Duration\"] = pd.Series([], dtype=float)\n",
" re[\"Round\"] = pd.Series(np.array(np.zeros(re.size), dtype=\"uint8\"))\n",
" rg['CamContXPos'] = pd.Series([], dtype=object)\n",
" rg['CamContYPos'] = pd.Series([], dtype=object)\n",
" rg['CamContZPos'] = pd.Series([], dtype=object)\n",
"\n",
" for index, re_row in re.copy().iterrows():\n",
" if re_row.TaskStatus == 'visible':\n",
" started = re_row.Timestamp\n",
" rg_info = rg.loc[(rg.RoundType == re_row.RoundType) & (rg.Trial == re_row.Trial)].iloc[0]\n",
" re.loc[index,'Round'] = rg_info.Round\n",
"\n",
" is_corresponding_event = (re.EventId == re_row.EventId) & (re.TaskStatus != 'visible')\n",
" ce_idx = re.index[is_corresponding_event]\n",
" \n",
" if ce_idx.size > 0:\n",
" corresponding_event = re.loc[ce_idx].iloc[0]\n",
" finished = corresponding_event.Timestamp\n",
" duration = finished - started\n",
" status = corresponding_event.TaskStatus\n",
" re.drop(ce_idx, inplace=True)\n",
" else:\n",
" print('unfinshed event') \n",
" finished = np.nan\n",
" duration = np.nan\n",
" status = 'unfinished'\n",
" \n",
" re.loc[index,'EndTime'] = finished\n",
" re.loc[index, 'Duration'] = duration\n",
" re.loc[index, 'TaskStatus'] = status\n",
" \n",
" re = re.rename(columns = {'Timestamp': 'StartTime'})\n",
"\n",
" return re, rg"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def fix_timestamps(ev, ga, ro):\n",
" total_dur = 0\n",
" for r in range(ro.Round.max() + 1):\n",
" curr_round = ro.loc[ro.Round == r]\n",
" trials = curr_round.Trial.max() + 1\n",
" \n",
" for t in range(trials):\n",
" trial_dur = curr_round.loc[curr_round.Trial == t].iloc[0].Duration\n",
" trial_mask = (ro.Round == r) & (ro.Trial == t)\n",
" ro.loc[trial_mask, 'StartTime'] = total_dur\n",
" ro.loc[trial_mask, 'EndTime'] = total_dur + trial_dur\n",
" \n",
" ga_trial_mask = (ga.Round == r) & (ga.Trial == t)\n",
" ev_trial_mask = (ev.Round == r) & (ev.Trial == t)\n",
" \n",
" ga_trial_start = ga.loc[ga_trial_mask].iloc[0].Timestamp\n",
" trial_time_offset = total_dur - ga_trial_start\n",
" \n",
" ga.loc[ga_trial_mask, 'Timestamp'] = ga.loc[ga_trial_mask, 'Timestamp'] + trial_time_offset\n",
" ev.loc[ev_trial_mask, 'StartTime'] = ev.loc[ev_trial_mask, 'StartTime'] + trial_time_offset\n",
" ev.loc[ev_trial_mask, 'EndTime'] = ev.loc[ev_trial_mask, 'EndTime'] + trial_time_offset\n",
" \n",
" total_dur += trial_dur + 0.001\n",
" return ro, ev, ga"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def preprocess(re, rg):\n",
" re = re.rename(columns = {\"EventInfo\" : \"TaskPos\", \"EventType\": \"TaskType\", \"EventStatus\": 'TaskStatus'})\n",
" \n",
" re, rg = move_custom_data(re, rg)\n",
" re, rg = add_rc_count_to_events(re, rg)\n",
" ro, re, rg = create_rounds(re, rg)\n",
" re, rg = process_events(re, rg)\n",
" ro, re, rg = fix_timestamps(re, rg, ro)\n",
" \n",
" re = re[['SubjectId', 'EventId', 'Round', 'RoundType', 'Trial', 'ValidTrial', 'TaskType', 'TaskStatus', 'TaskPos', 'Duration', 'StartTime', 'EndTime']]\n",
" rg = rg[['SubjectId', 'Timestamp', 'Round', 'Trial', 'PlayerXPos', 'PlayerYPos', 'PlayerZPos', 'MainCamXPos', 'MainCamYPos', 'MainCamZPos', 'PlayerXRot', 'PlayerYRot', 'PlayerZRot', 'MainCamXRot', 'MainCamYRot', 'MainCamZRot', 'CamContXPos', 'CamContYPos', 'CamContZPos']]\n",
"\n",
" return ro, re, rg"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def create_final_csvs(write_csvs=False):\n",
" study_dict = { 'rounds': [], 'events': [], 'gamestatus': [] }\n",
" \n",
" for subject in range(4):\n",
" # check if data for the subject available\n",
" if len(glob.glob('study_data/s%d_*.csv' % subject)) < 2:\n",
" continue\n",
" \n",
" print('Subject #%d' % (subject))\n",
" \n",
" # load all csvs and concatenate splits if available\n",
" raw_events, raw_gamestatus = load_csvs(subject)\n",
" \n",
" # preprocess data\n",
" ro, ev, ga = preprocess(raw_events, raw_gamestatus)\n",
" \n",
" # add to data dict\n",
" study_dict['rounds'].append(ro)\n",
" study_dict['events'].append(ev)\n",
" study_dict['gamestatus'].append(ga)\n",
" \n",
" # clean index\n",
" ro_total = pd.concat(study_dict['rounds']).reset_index()\n",
" ev_total = pd.concat(study_dict['events']).reset_index()\n",
" gs_total = pd.concat(study_dict['gamestatus']).reset_index()\n",
" \n",
" if write_csvs:\n",
" ro_total.to_excel('ro_all.xlsx')\n",
" ev_total.to_excel('ev_all.xlsx')\n",
" gs_total.to_excel('gs_all.xlsx')\n",
" \n",
" print('finished')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Subject #3\n",
"['-0.313', ' -1.219', ' -0.412']\n",
"unfinshed event\n",
"finished\n"
]
}
],
"source": [
"create_final_csvs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data-Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"ro_tot = pd.read_excel('rounds_total.xlsx')\n",
"ev_tot = pd.read_excel('events_total.xlsx')\n",
"gs_tot = pd.read_excel('gamestatus_total.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_motion_data(gs, started, finished):\n",
" gidx = gs.index[(gs.Timestamp >= started) & (gs.Timestamp <= finished)]\n",
" md = gs.loc[gidx, ['PlayerYRot', 'PlayerXRot', 'MainCamYRot', 'MainCamXRot']]\n",
" md['MainCamXRotRel'] = md['MainCamXRot'] - md['PlayerXRot']\n",
" md['MainCamYRotRel'] = md['MainCamYRot'] - md['PlayerYRot']\n",
" md['MainCamXRotNorm'] = md.apply(lambda row: ((row.MainCamXRotRel - 180) % 360 - 180), axis=1)\n",
" return md"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gewinnspiel Auswertung"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"columns = ['SubjectId', 'RoundType', 'RoundScore', 'PovSucc', 'PovTotal', 'RingSucc', 'RingTotal']\n",
"data = []\n",
"\n",
"def filter_ev(group):\n",
" return ((group.Trial == group.Trial.max())\n",
" & group.ValidTrial \n",
" & (~group.RoundType.isin(['Training_Ring_Only', 'Training_Complete']))).any()\n",
"\n",
"def round_score(x):\n",
" pov_succ = x[(x.TaskType == 'POV') & (x.TaskStatus == 'success')].EventId.size\n",
" pov_tot = x[(x.TaskType == 'POV')].EventId.size\n",
" ring_succ = x[(x.TaskType == 'Ring') & (x.TaskStatus == 'success')].EventId.size\n",
" ring_tot = x[(x.TaskType == 'Ring')].EventId.size\n",
" round_score = pov_succ * (ring_succ / ring_tot)\n",
" data.append({'SubjectId': x.iloc[0].SubjectId, 'RoundType': x.iloc[0].RoundType, \n",
" 'RoundScore': round_score, 'PovSucc': pov_succ, 'PovTotal': pov_tot,\n",
" 'RingSucc': ring_succ, 'RingTotal': ring_tot})\n",
" return round_score\n",
"\n",
"grouped = ev_tot.groupby(['SubjectId', 'RoundType', 'Trial'])\n",
"ev_red = grouped.filter(filter_ev)\n",
"\n",
"#print(ev_red[(ev_red.SubjectId == 3) & (ev_red.TaskType == 'POV')])\n",
"\n",
"round_score_group = ev_red.groupby(['SubjectId', 'RoundType']).apply(round_score)\n",
"total_score = round_score_group.groupby(['SubjectId']).agg({'Sum': 'sum'}).sort_values(by=\"SubjectId\", ascending=True)\n",
"print(total_score)\n",
"\n",
"#df = pd.DataFrame(data=data, columns=columns)\n",
"#df.to_excel('scores.xlsx')\n",
"\n",
"#print(filtered_ev_tot.groupby(['SubjectId', 'RoundType', 'TaskType', 'TaskStatus'])['EventId'].agg({\"Count\": 'count'}).to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### POV Selection Times by Round Type"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pov = ev_tot[(ev_tot.RoundType != 'Training_Complete') &(ev_tot.TaskType == 'POV') & (ev_tot.TaskStatus == 'success')]\n",
"# Fehlerhafte EInträge entfernen\n",
"pov = pov.drop(pov[(pov.RoundType == 'Audio') & (pov.Duration > 10)].index)\n",
"plot = pov.boxplot(column=['Duration'], by='RoundType', figsize=(20,10))\n",
"fig = plot.get_figure()\n",
"fig.savefig(\"output.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Feedback Reaction Times"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for rt in ev_tot.RoundType.unique():\n",
" round_ev = ev_tot[ev_tot.RoundType == rt]\n",
" round_pov = events[events.TaskType == 'POV']\n",
" #pov_succ = pov_events.loc[events.Status == 'success']\n",
" \n",
" #ring_events = events.loc[(eve nts.TaskType == 'Ring')]\n",
" #ring_succ = ring_events.loc[events.Status == 'success'].shape[0]\n",
" #ring_fail = ring_events.loc[events.Status == 'timeout'].shape[0]\n",
" #pov_fail = pov_events.loc[events.Status == 'timeout'].shape[0]\n",
"\n",
" for pos in round_pov.Position.unique():\n",
" print(\"Plot for %s, %s\" % (rt, pos))\n",
" round_pov_pos = round_pov[round_pov.Position == pos]\n",
" round_pov_pos.plot(kind=\"scatter\", x=\"MainCamYRot\", y=\"MainCamXRot\", xlim=(-360,360), ylim=(180,-180))\n",
" plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.is_functional.ipynb | 1 | 35592 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# _automaton_.is_functional"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Whether the automaton is _functional_, i.e. each input (string) is transduced to a unique output (string). There may be multiple paths, however, that contain this input and output string pair.\n",
"\n",
"Precondition:\n",
"- The automaton is transducer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examples"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
]
},
{
"data": {
"application/javascript": [
"IPython.load_extensions(\"AutomatonD3Widget\")"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import vcsn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple Cases"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"%%automaton a\n",
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"This transducer is functional, as can also be seen from its series (computed thanks to _automaton_.shortest): it uniquely maps `ab` to `xy`."
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"However, the following transducer is _not_ functional, as it maps `ab` to both `xy` and `xz`, again, as demonstrated by `shortest`."
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"%%automaton a\n",
"context = \"lat<lal_char(abc),lal_char(xyz)>, b\"\n",
"$ -> 0\n",
"0 -> 1 a|x\n",
"0 -> 2 a|x\n",
"1 -> 3 b|y\n",
"2 -> 3 b|z\n",
"3 -> $"
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"### A More Complex Example"
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"source": [
"The following example (Figure 3 from [beal.2003.tcs](References.ipynb#beal.2003.tcs)) shows a transducer whose _input automaton_ is ambiguous, yet the transduder is functional."
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"%%automaton a\n",
"context = \"lat<lal_char(a),law_char(x)>, b\"\n",
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| gpl-3.0 |
mluessi/scientific-python-intro | scientific-python-intro.ipynb | 1 | 121238 | {
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"cells": [
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"input": [
"# import some things\n",
"%pylab --no-import-all inline\n",
"import numpy as np\n",
"import pylab as pl\n",
"from scipy import linalg"
],
"language": "python",
"metadata": {},
"outputs": [
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"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
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},
{
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"metadata": {
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"source": [
"Python for Scientific Computing\n",
"===============================\n",
"\n",
"<img src=\"http://www.python.org/images/python-logo.gif\">\n",
"<img src=\"http://www.scipy.org/_static/images/scipy_med.png\">\n",
"\n",
"**Martin Luessi**\n",
"\n",
"**Martinos Center \"Why N' How\", September 19, 2013**"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"What is Python and why would I use it?\n",
"--------------------------------------\n",
"\n",
"- Python is an **intepreted high-level programming language**\n",
"- Python is **free** (as in speech)\n",
"- Python runs on most platforms\n",
"- It [**\"combines remarkable power with very clear syntax\"**](http://docs.python.org/faq/general.html#what-is-python)\n",
"- Well suited for **high performance numerical computing** (NumPy, ...)\n",
"- High quality **2D and 3D visualizations** (pylab, mlab, ...)\n",
"- Increasingly **popular in neuroscience** (nipy, nipype, nitime, ...)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"What you should be able to do after this talk\n",
"---------------------------------------------\n",
"\n",
"- Start Python\n",
"- Do simple math\n",
"- Get started with linear algebra and scientific computing\n",
"- Plot some nice figures"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Use Python for what?\n",
"--------------------\n",
"\n",
"- Scripting (like shell scripts, e.g., bash, csh)\n",
"- Make web sites\n",
"- Build GUI applications\n",
"- **Science** (like Matlab, IDL, R, Octave, Scilab)\n",
"- Etc.\n",
"\n",
"**You just need to know one language to do almost anything !**\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Scientific Python building blocks\n",
"---------------------------------\n",
"\n",
"* **Python interpreter**: executes Python code\n",
"\n",
"* [**IPython**](http://ipython.org): an advanced **Python shell**\n",
"\n",
"* [**NumPy**](http://www.numpy.org): provides **numerical array** objects\n",
"\n",
"* [**SciPy**](http://www.scipy.org/): scientific computing\n",
" (linear algebra, optimization, regression, etc.)\n",
"\n",
"* [**Matplotlib**](http://matplotlib.org) a.k.a. Pylab: 2-D visualization, \"publication-ready\" plots\n",
"\n",
"* [**Mayavi**](http://mayavi.sourceforge.net) : 3-D visualization\n",
"\n",
"* Many application specific packages for e.g., machine learning,\n",
" image processing, symbolic math, .. [incomplete list](http://www.scipy.org/Topical_Software)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"First Steps\n",
"-----------\n",
"\n",
"Get a scientific-Python environment:\n",
"\n",
"* Comes with every Linux distribution\n",
"* Python(x,y) on Windows: http://www.pythonxy.com\n",
"* Enthought Canopy or EPD: http://www.enthought.com\n",
"* Continuum Analytics Anaconda http://www.continuum.io\n",
"* At the Martinos Center use the EPD based network installation, see [here](http://surfer.nmr.mgh.harvard.edu/fswiki/DevelopersGuide/NMRCenterPython/UsersGuide\n",
")\n",
"\n",
"Start the **IPython shell** (from terminal or Windows cmd shell):\n",
"\n",
" $ ipython --pylab"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Hello world!\n",
"------------\n",
"\n",
"The IPython Shell is an interactive shell:\n",
"\n",
"<img src=\"http://ipython.org/ipython-doc/stable/_images/colors_dark.png\" width=\"400\" height=\"400\"/> \n",
"\n",
"Now we can write our \"Hello World\" program by typing:"
]
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"print s"
],
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}
},
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"output_type": "stream",
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]
}
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},
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"My first script\n",
"---------------\n",
"\n",
"Let's say the file ``my_script.py`` contains:\n",
"\n",
" s = 'Hello World!'\n",
" print s\n",
" \n",
"In IPython you can run it as follows:"
]
},
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],
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}
},
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"output_type": "stream",
"stream": "stdout",
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]
}
],
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"source": [
"If you are scared of the terminal\n",
"---------------------------------\n",
"\n",
"You can use [**Spyder**](http://code.google.com/p/spyderlib), a scientific Python IDE.\n",
"Or the [**IPython Notebook**](http://ipython.org/notebook.html)\n",
"\n",
"\n",
"<div style=\"float: left\">\n",
"<img src=\"http://wiki.spyderlib.googlecode.com/hg/Screenshots/spyder-linux_small.png\" width=\"420\"/> \n",
"</div>\n",
"<div style=\"float: right\">\n",
"<img src=\"http://ipython.org/_images/9_home_fperez_prof_grants_1207-sloan-ipython_proposal_fig_ipython-notebook-specgram.png\" width=\"420\"/> \n",
"</div>\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
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}
},
"source": [
"Start the **IPython Notebook** as follows\n",
"\n",
" $ ipython notebook --pylab=inline"
]
},
{
"cell_type": "markdown",
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},
"source": [
"Python basics: Numerical types\n",
"------------------------------\n",
"\n",
"Integer variables:\n",
"\n",
" >>> 1 + 1\n",
" 2\n",
" >>> a = 4\n",
"\n",
"floats:\n",
"\n",
" >>> c = 2.1\n",
"\n",
"complex (a native type in Python!):\n",
"\n",
" >>> a = 1.5 + 0.5j\n",
" >>> a.real\n",
" 1.5\n",
" >>> a.imag\n",
" 0.5"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Python basics: Numerical types\n",
"------------------------------\n",
"\n",
"and booleans:\n",
"\n",
" >>> 3 < 4\n",
" True\n",
" >>> test = (3 > 4)\n",
" >>> test\n",
" False\n",
" >>> type(test)\n",
" <type 'bool'>\n",
"\n",
"Note that **you don't need to specify the type** of the variable\n",
"\n",
" int a = 1; # in C"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Python basics: Numerical types\n",
"------------------------------\n",
"\n",
"Python can replace your pocket calculator with : ``+``, ``-``, ``*``, ``/``, ``%`` (modulo)\n",
"\n",
" >>> 7 * 3.\n",
" 21.0\n",
" >>> 2**10\n",
" 1024\n",
" >>> 8 % 3\n",
" 2\n",
"\n",
"**WARNING** : Integer division\n",
"\n",
" >>> 3 / 2 # !!!\n",
" 1\n",
" >>> 3 / 2. # Trick: use floats\n",
" 1.5\n",
" >>> 3 / float(2) # type conversion\n",
" 1.5"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Strings\n",
"-------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_str = 'Hello World!'\n",
"print my_str\n",
"print my_str[0]"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello World!\n",
"H\n"
]
}
],
"prompt_number": 183
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Notice**: Indexing in Python starts at zero (like in C)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Strings are **objects** with many useful **methods**:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print my_str.replace('World', 'Why N\\' How')\n",
"print my_str.upper()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello Why N' How!\n",
"HELLO WORLD!\n"
]
}
],
"prompt_number": 184
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Container types: list\n",
"---------------------\n",
"\n",
"An **ordered** container that can hold arbitrart Python objects"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list = [1, 2, 3, 'test'] # Notice: [] creates a list\n",
"print my_list"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 2, 3, 'test']\n"
]
}
],
"prompt_number": 185
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We can append and insert things"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list.append('test2')\n",
"print my_list\n",
"my_list.insert(1, 0)\n",
"print my_list\n"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 2, 3, 'test', 'test2']\n",
"[1, 0, 2, 3, 'test', 'test2']\n"
]
}
],
"prompt_number": 186
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Container types: list\n",
"---------------------\n",
"\n",
"We can access elements using their index"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print my_list\n",
"print my_list[0] # first element\n",
"print my_list[-1] # last element\n",
"print my_list[-2] # second last element\n"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 0, 2, 3, 'test', 'test2']\n",
"1\n",
"test2\n",
"test\n"
]
}
],
"prompt_number": 187
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Container types: list\n",
"---------------------\n",
"\n",
"We can also use **slicing** to obtain sublists"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print my_list\n",
"print my_list[2:5] # Notice: index 5 is not included"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 0, 2, 3, 'test', 'test2']\n",
"[2, 3, 'test']\n"
]
}
],
"prompt_number": 188
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"The slicing syntax is `l[start:stop:step]`. This can be very useful"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print my_list[:3] # first 3 elements\n",
"print my_list[-3:] # last 3 elements\n",
"print my_list[::2] # every 2nd element\n",
"print my_list[::-1] # list with order reversed"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 0, 2]\n",
"[3, 'test', 'test2']\n",
"[1, 2, 'test']\n",
"['test2', 'test', 3, 2, 0, 1]\n"
]
}
],
"prompt_number": 189
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Container types: dictionary\n",
"---------------------------\n",
"\n",
"A dictionary (``dict``) is basically an efficient table that **maps keys to\n",
"values**. It is an **unordered** container:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"phone = {'joe': 554, 'bob': 308} # using {} creates a dict\n",
"print phone # Notice: no order"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'bob': 308, 'joe': 554}\n"
]
}
],
"prompt_number": 190
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We can access elements using their **key**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print phone['joe']"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"554\n"
]
}
],
"prompt_number": 191
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"And add new elements (**Notice**: key does not have to be a string)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"phone[0] = 101\n",
"print phone\n",
"print phone.keys() # list with the keys\n",
"print phone.values() # list with the values"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{0: 101, 'bob': 308, 'joe': 554}\n",
"[0, 'bob', 'joe']\n",
"[101, 308, 554]\n"
]
}
],
"prompt_number": 192
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Basic control flow: Conditional statements\n",
"------------------------------------------\n",
"\n",
"Allow the conditional execution of code"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = 10\n",
"if a == 1:\n",
" print 1\n",
" print 22\n",
"elif a == 2:\n",
" print 2\n",
"else:\n",
" print 'a lot'"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"a lot\n"
]
}
],
"prompt_number": 193
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Notice**: Blocks are delimited by indentation (4 spaces)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Basic control flow: Loops\n",
"-----------------------------\n",
"\n",
"Can be used to iterate over lists, dicts, etc. For example:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for word in ['cool', 'powerful', 'readable']:\n",
" print 'Python is %s !!!' % word"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Python is cool !!!\n",
"Python is powerful !!!\n",
"Python is readable !!!\n"
]
}
],
"prompt_number": 194
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"My first function\n",
"-----------------\n",
"\n",
"Functions are defined using **def**, they allow us to group code for specific tasks."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def disk_area(radius):\n",
" area = 3.14 * radius * radius\n",
" return area\n",
"\n",
"print disk_area(1.0)\n",
"print disk_area(2.0)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3.14\n",
"12.56\n"
]
}
],
"prompt_number": 195
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"My second function\n",
"------------------\n",
"\n",
"**Arguments are not copied** when passed to a function (not like with Matlab)\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import copy\n",
"\n",
"def foo(a):\n",
" a.append(1) \n",
"\n",
"b = [0]\n",
"foo(b)\n",
"print b # a has been modified !!!"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0, 1]\n"
]
}
],
"prompt_number": 196
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: N-dimensional arrays in Python\n",
"-------------------------------------\n",
"\n",
"**NumPy** is:\n",
"\n",
"* An extension package to Python for multidimensional arrays (matrices in n-dimensions)\n",
"* Designed for **efficient** scientific computation\n",
"* Unlike Python lists, all elements of the array have the same type (int, float, etc)\n",
"\n",
"Reference documentation: http://docs.scipy.org/doc/numpy/reference\n",
"\n",
"For Matlab users: http://wiki.scipy.org/NumPy_for_Matlab_Users"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Creating arrays\n",
"----------------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np # import numpy so we can use it\n",
"a = np.array([0, 1, 2, 3], dtype=np.float) # create array\n",
"print a\n",
"\n",
"print a.ndim # number of dimensions, in Matlab `ndims(a)`\n",
"print a.shape # shape, in Matlab `size(a)`\n",
"print a.dtype # the data type of the array"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0. 1. 2. 3.]\n",
"1\n",
"(4,)\n",
"float64\n"
]
}
],
"prompt_number": 197
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Creating arrays\n",
"----------------------\n",
"\n",
"Arrays can have an arbitrary number of dimensions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 2-D array\n",
"b = np.array([[0, 1, 2], [3, 4, 5]]) # 2 x 3 array\n",
"print b\n",
"print b.dtype # Notice: here the data type is int64\n",
"print b.shape"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[0 1 2]\n",
" [3 4 5]]\n",
"int64\n",
"(2, 3)\n"
]
}
],
"prompt_number": 198
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 3-D\n",
"c = np.array([[[1], [2]], [[3], [4]]])\n",
"print c.shape # in Matlab `size(c)`"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(2, 2, 1)\n"
]
}
],
"prompt_number": 199
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Creating arrays\n",
"----------------------\n",
"\n",
"* Common arrays: **ones**, **zeros** and **eye** (like in Matlab)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.ones((3, 3))\n",
"print a"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 1. 1. 1.]\n",
" [ 1. 1. 1.]\n",
" [ 1. 1. 1.]]\n"
]
}
],
"prompt_number": 200
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = np.zeros((2, 3))\n",
"print b"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0. 0. 0.]\n",
" [ 0. 0. 0.]]\n"
]
}
],
"prompt_number": 201
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"c = np.eye(3)\n",
"print c"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 1. 0. 0.]\n",
" [ 0. 1. 0.]\n",
" [ 0. 0. 1.]]\n"
]
}
],
"prompt_number": 202
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Indexing and slicing\n",
"---------------------------\n",
"\n",
"NumPy arrays can be **indexed** and **sliced** like Python lists"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.diag(np.arange(3))\n",
"print a"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[0 0 0]\n",
" [0 1 0]\n",
" [0 0 2]]\n"
]
}
],
"prompt_number": 203
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print a[1, 1]\n",
"print a[:,1] # takes the entire second row!"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n",
"[0 1 0]\n"
]
}
],
"prompt_number": 204
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# slicing\n",
"a = np.arange(10)\n",
"print a\n",
"print a[::2] # every 2nd element\n",
"print a[-5:] # last 5 elements"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0 1 2 3 4 5 6 7 8 9]\n",
"[0 2 4 6 8]\n",
"[5 6 7 8 9]\n"
]
}
],
"prompt_number": 205
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Copies and views\n",
"-----------------------\n",
"\n",
"* A slicing operation creates a **view** on the original array"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.arange(10)\n",
"print a\n",
"b = a[::2]\n",
"print b"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0 1 2 3 4 5 6 7 8 9]\n",
"[0 2 4 6 8]\n"
]
}
],
"prompt_number": 206
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* **The original array is not copied in memory: when modifying the view, the original array is modified as well.**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b[0] = 100\n",
"print b\n",
"print a # a was modified as well!"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[100 2 4 6 8]\n",
"[100 1 2 3 4 5 6 7 8 9]\n"
]
}
],
"prompt_number": 207
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Copies and views\n",
"-----------------------\n",
"\n",
"If you want a copy you have to specify it:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.arange(10)\n",
"b = a[::2].copy() # force a copy\n",
"b[0] = 100\n",
"print b\n",
"print a"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[100 2 4 6 8]\n",
"[0 1 2 3 4 5 6 7 8 9]\n"
]
}
],
"prompt_number": 208
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"This behavior can be surprising at first sight...\n",
"\n",
"but it allows to **save both memory and time**."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: File formats\n",
"-------------------\n",
"\n",
"NumPy has its own file format for saving and loading arrays:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.arange(10)\n",
"np.save('test.npy', a)\n",
"a = 0\n",
"a = np.load('test.npy')\n",
"print a"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0 1 2 3 4 5 6 7 8 9]\n"
]
}
],
"prompt_number": 209
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"But Python supports well-known (& more obscure) file formats:\n",
" \n",
"* Matlab: ``scipy.io.loadmat``, ``scipy.io.savemat``\n",
"* HDF5: [``h5py``](http://code.google.com/p/h5py), [``PyTables``](http://pytables.org)\n",
"* NetCDF: ``scipy.io.netcdf_file``,\n",
"[``netcdf4-python``](http://code.google.com/p/netcdf4-python)\n",
"\n",
"* MatrixMarket: ``scipy.io.mmread``, ``scipy.io.mmread``\n",
".. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Linear algebra\n",
"---------------------\n",
"\n",
"Matrix multiplication:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.triu(np.ones((2, 2)), 1) # see help(np.triu)\n",
"print 'a:' + str(a)\n",
"b = np.diag([1, 2])\n",
"print 'b:' + str(b)\n",
"c = np.dot(a, b) # same as a.dot(b)\n",
"print 'c:' + str(c)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"a:[[ 0. 1.]\n",
" [ 0. 0.]]\n",
"b:[[1 0]\n",
" [0 2]]\n",
"c:[[ 0. 2.]\n",
" [ 0. 0.]]\n"
]
}
],
"prompt_number": 210
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**WARNING**: Element-wise multiplication vs. matrix multiplication"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print a * b # element-wise multiplication"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0. 0.]\n",
" [ 0. 0.]]\n"
]
}
],
"prompt_number": 211
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Linear algebra\n",
"---------------------\n",
"\n",
"Transpose:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_t = a.T\n",
"print a_t"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0. 0.]\n",
" [ 1. 0.]]\n"
]
}
],
"prompt_number": 212
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Note: As with slicing, there is no copy. We can verify this by inspecting the arrays:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'a.flags:\\n' + str(a.flags)\n",
"print 'a_t.flags:\\n' + str(a_t.flags)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"a.flags:\n",
" C_CONTIGUOUS : True\n",
" F_CONTIGUOUS : False\n",
" OWNDATA : True\n",
" WRITEABLE : True\n",
" ALIGNED : True\n",
" UPDATEIFCOPY : False\n",
"a_t.flags:\n",
" C_CONTIGUOUS : False\n",
" F_CONTIGUOUS : True\n",
" OWNDATA : False\n",
" WRITEABLE : True\n",
" ALIGNED : True\n",
" UPDATEIFCOPY : False\n"
]
}
],
"prompt_number": 213
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Linear algebra\n",
"---------------------\n",
"\n",
"Inverse, systems of linear equations and SVD:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import linalg # OR\n",
"from scipy import linalg # even better\n",
"A = np.triu(np.ones((3, 3)), 0)\n",
"print 'A:\\n' + str(A)\n",
"B = linalg.inv(A)\n",
"C = np.dot(B, A)\n",
"print 'C:\\n' + str(C)\n",
"x = linalg.solve(A, [1, 2, 3]) # linear system\n",
"U, s, V = linalg.svd(A) # SVD\n",
"vals = linalg.eigvals(A) # Eigenvalues"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"A:\n",
"[[ 1. 1. 1.]\n",
" [ 0. 1. 1.]\n",
" [ 0. 0. 1.]]\n",
"C:\n",
"[[ 1. 0. 0.]\n",
" [ 0. 1. 0.]\n",
" [ 0. 0. 1.]]\n"
]
}
],
"prompt_number": 214
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"NumPy: Reductions\n",
"-----------------\n",
"\n",
"Computing sums:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.arange(5)\n",
"print x\n",
"print np.sum(x) # or x.sum()\n",
" "
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0 1 2 3 4]\n",
"10\n"
]
}
],
"prompt_number": 215
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Sum by rows and by columns:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.array([[1, 1], [2, 2]])\n",
"print np.sum(x, axis=0), # columns (first dimension)\n",
"print np.sum(x, axis=1) # rows (second dimension)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[3 3] [2 4]\n"
]
}
],
"prompt_number": 216
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Same with ``np.mean, np.argmax, np.argmin, np.min, np.max, np.cumsum, np.sort`` etc."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"SciPy\n",
"-----\n",
"\n",
"* ``scipy`` contains various toolboxes dedicated to common issues in scientific computing.\n",
"\n",
"* ``scipy`` can be compared to other standard scientific-computing libraries, such as the GSL (GNU Scientific Library for C and C++), or Matlab's toolboxes.\n",
"\n",
"* ``scipy`` is the core package for scientific routines in Python.\n",
"\n",
"* ``scipy`` is meant to operate efficiently on ``numpy`` arrays."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"SciPy\n",
"-----\n",
"\n",
"* ``scipy.io`` for IO (e.g. read / write Matlab files)\n",
"* ``scipy.linalg`` for optimized linear algebra\n",
"* ``scipy.stats`` for basic stats (t-tests, simple anova, ranksum etc.)\n",
"* ``scipy.signal`` for signal processing\n",
"* ``scipy.sparse`` for sparse matrices\n",
"* ``scipy.fftpack`` for FFTs\n",
"* ``scipy.ndimage`` for N-D image processing (e.g., smoothing)\n",
"* etc."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"SciPy: Example of ``scipy.stats``\n",
"---------------------------------\n",
"\n",
"A T-test to decide whether the two sets of observations have different means:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy import stats\n",
"a = np.random.normal(0, 1, size=10)\n",
"b = np.random.normal(1, 1, size=10)\n",
"tval, pval = stats.ttest_ind(a, b)\n",
"print 'T=%0.4f, p=%0.4f' % (tval, pval)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"T=-3.0062, p=0.0076\n"
]
}
],
"prompt_number": 217
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Visualization with Python\n",
"-------------------------\n",
"\n",
"Matplotlib provides functions to create publication-quality figures"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pylab as pl\n",
"t = np.linspace(0, 8 * np.pi, 1000)\n",
"pl.plot(t, np.sin(t))\n",
"pl.xlabel('$x$')\n",
"pl.ylabel('$sin(x)$')\n",
"pl.ylim([-1.1, 1.1])\n",
"pl.savefig('pylab_demo.pdf') # natively save pdf, svg, etc."
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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ckosI5rIGhc4B04RdCPymupqWjBG99XB3Og8ZF+GXlhbgm2/kE1jghl8mTxZz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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x2aa63d0>"
]
}
],
"prompt_number": 218
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Visualization with Python\n",
"-------------------------\n",
"\n",
"* 2-D (such as images)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"image = np.random.rand(30, 30)\n",
"pl.imshow(image)\n",
"pl.gray()\n",
"pl.show()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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noS+PffLJJ8sGkp1OJ77whS9U/u+AZxLcwkdMlyq6aw4x4ULR7XaLq60a1kHP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\nX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"text": [
"<matplotlib.figure.Figure at 0x44f6510>"
]
}
],
"prompt_number": 219
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Visualization with Python\n",
"-------------------------\n",
"\n",
"* [**Mayavi**](http://mayavi.sourceforge.net) : 3-D visualization\n",
"\n",
"<img src=\"http://docs.enthought.com/mayavi/mayavi/_images/example_surface_from_irregular_data.jpg\" width=240>\n",
"\n",
"* [**PySurfer**](http://pysurfer.github.com) uses Mayavi to visualize cortical surfaces\n",
"\n",
"\n",
"<div style=\"float: left\">\n",
"<img src=\"http://pysurfer.github.io/_images/plot_fmri_contours.png\" width=200>\n",
"<img src=\"http://pysurfer.github.io/_images/plot_labels.png\" width=200>\n",
"<img src=\"http://pysurfer.github.io/_images/plot_meg_inverse_solution.png\" width=415>\n",
"</div>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Learn more\n",
"----------\n",
"\n",
"- http://scipy-lectures.github.com\n",
"- http://www.scipy.org/NumPy_for_Matlab_Users\n",
"\n",
"Even more:\n",
"\n",
"- Matlab like IDE environment: http://packages.python.org/spyder\n",
"- Parallel computing: http://packages.python.org/joblib\n",
"- Cython: write Python get C code http://cython.org"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Python for brain imaging\n",
"------------------------\n",
"\n",
"- [**NiBabel**](http://nipy.sourceforge.net/nibabel) for handling neurimaging file formats\n",
"- [**Nipype**](http://nipy.sourceforge.net/nipype) Pipeline for SPM, FSL, FreeSurfer, etc.\n",
"- [**PySurfer**](http://pysurfer.github.com) visualization of FreeSurfer surfaces\n",
"- [**MNE-Python**](http://martinos.org/mne) MEG and EEG data analysis\n",
"- [**scikit-learn**](http://scikit-learn.org) Machine learning and statistics\n",
"- [**NiLearn**](http://nilearn.github.io) Machine learning for neuroimaging (uses scikit-learn)\n",
"- [**NIPY**](http://www.nipy.org) various neuroimaging packages\n",
"- etc.\n",
"\n",
"A really active community !"
]
}
],
"metadata": {}
}
]
} | unlicense |
RaoUmer/opendatasci | notebooks/A1. Linear Regression - Overview.ipynb | 3 | 63246 | {
"metadata": {
"name": "A1. Linear Regression - Overview"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear Regression - Overview\n",
"==================================\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How can I make predictions about real-world quantities, like sales or life expectancy?\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most often in real world applications we need to understand how one variable is determined by a number of others.\n",
"\n",
"For example:\n",
"\n",
"* How does sales volume change with changes in price. How is this affected by changes in the weather?\n",
"\n",
"* How does the amount of a drug absorbed vary with dosage and with body weight of patient? Does it depend on blood pressure?\n",
"\n",
"* How are the conversions on an ecommerce website affected by two different page titles in an A/B comparison? \n",
"\n",
"* How does the energy released by an earthquake vary with the depth of it's epicenter?\n",
"\n",
"* How is the interest rate charged on a loan affected by credit history and by loan amount?\n",
"\n",
"Answering questions like these, requires us to create a **model**. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A model is a formula where one variable (response) varies depending on one or more independent variables (covariates). For the loan example, interest rate might depend on FICO score, state, loan amount, and loan duration amongst others.\n",
"\n",
"One of the simplest models we can create is a **Linear Model** where we start with the assumption that the dependent variable varies linearly with the independent variable(s).\n",
"\n",
"While this may appear simplistic, many real world problems can be modeled usefully in this way. Often data that don't appear to have a linear relationship can be transformed using simple mappings so that they do now show a linear relationship. This is very powerful and Linear Models, therefore, have wide applicability. \n",
"\n",
"They are one of the foundational tools of Data Science.\n",
"\n",
"Creating a Linear Model involves a technique known as **Linear Regression**. It's a tool you've most probably already used without knowing that's what it was called.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Linear Regression in the high school physics lab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember a typical physics lab experiment from high school? We had some input X (say force) which gave some output Y (say acceleration). \n",
"\n",
"You made a number of pairs of observations x, y and plotted them on graph paper."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<image src=\"files/images/a1fig1_labexperiment.png\" />"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then you had to fit a straight line through the set of observations using a visual \"best fit\". "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<image src=\"files/images/a1fig2_labexperiment_withline.png\" />"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then you read off 'm' the slope, and 'b', the y-intercept from the graph, hoping it was close to the expected answer. By drawing the \"best fit\" line you were, in effect, visually estimating m and b without knowing it. \n",
"\n",
"You were doing informal Linear Regression. We're going to do this a little more formally. And then make it more sophisticated."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now for a bit of math\n",
"\n",
"Let's start with the basics.\n",
"\n",
"Remember the equation for a straight line from high school? \n",
"\n",
"$$Y = mX + b$$\n",
"\n",
"where $m$ is the slope and $b$ is the y-intercept.\n",
"\n",
"Very briefly and simplistically, Linear Regression is a class of techniques for: \n",
"\n",
"**_fitting a straight line to a set of data points_**. \n",
"\n",
"This could also be considered \n",
"\n",
"**_reverse engineering a formula from the data_**.\n",
"\n",
"We'll develop this idea starting from first principles and adding mathematical sophistication as we go along. But before that, you're probably curious what were the 'm' and 'b' values for this graph. We use modeling software to generate this for us and we get:\n",
"\n",
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<image src=\"files/images/a1fig3_labexperiment_slopeintercept.png\" />"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see two numbers, \"Intercept\" and \"Slope\".\n",
"Independent of what software we use to do our linear regression for us, it will report these two numbers in one form or another.\n",
"The \"Intercept\" here is the \"b\" in our equation.\n",
"And the \"Slope\" is the slope of Y with respect to the independent variable.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To summarize, we have a dataset (the observations) and a model (our guess for a formula that fits the data) and we have to figure out the parameters of the model (the coefficients m and b in our best fit line) so that the model fits the data the \"best\". \n",
"We want to use our data to find coefficients for a formula so that the formula will fit the data the \"best\". \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we continue, we'll actually run the modeling software and generate these numbers from real data. Here we just saw pictures of the results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using the model for prediction \n",
"\n",
"Once you had your visual best fit line and had read off the m and b you probably said something to the effect: \n",
"\n",
"\"The data follows a linear equation of the form Y = mX + b where m (slope)=(somenumber) and b (y intercept)=(someothernumber)\"\n",
"\n",
"You may recall that the equation is not an exact representation because most probably your data points are not all in a perfectly straight line. So there is some error varying from one data point to the next data point. Your visual approach subjectively tried to minimize some intuitive \"total error\" over all the data. \n",
"\n",
"What you did was intuitive \"Linear Regression\". You estimated m and b by the \"looks right to me\" algorithm.\n",
"We will start with this intuitive notion and rapidly bring some heavy machinery to bear that will allow us to solve pretty sophisticated problems.\n",
"\n",
"At this point your lab exercise may well ask you to approximate what Y will be when X is some number outside the range of your measurements.\n",
"Then you use the equation above where m and b are now actual numbers say 2.1 and 0.3 respectively i.e the equation is Y = 2.1X + 0.3\n",
"\n",
"This equation is your \"model\"\n",
"\n",
"And you plug in an X to get a Y. \n",
"\n",
"This is where you are using your model to predict a value or, in other words, you are saying that I didn't use this value of X in my experiment and I don't have it in my data but I'd like to know what this value of X will map to on the Y axis.\n",
"\n",
"Based on my model Y = 2.1X + 0.3 if I had used this value in my experiment then I believe I would have got an output Y of approximately what the straight line suggests. \n",
"\n",
"You also want to be able to say \"my error is expected to be (some number), so I believe the actual value will lie between Y-error and Y+error\". \n",
"\n",
"When used like this we call the X variable the \"predictor\" as values of Y are **predicted** based one values of X, and the Y variable the \"response\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But before we do that let's take another trip back to the physics lab and peek over at the neighboring lab group's plots.\n",
"We might see a different plot.\n",
"So which one is \"correct\"? \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<image src=\"files/images/a1fig4_twolabexperiments.png\" />"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A notion of total error\n",
"\n",
"\n",
"Visually we can see that our plot (the first one) is the \"better\" one. But why?\n",
"Because intuitively we feel that the line is closer to the points in the first one.\n",
"So let's try to understand formally why that might be correct. Or not."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Actually the graphs above were plotted by software that generated some points with random variation and then plotted a line through them. \n",
"\n",
"What the software did was compute a function called a \"loss function\", a measure of error. Then, it \"tried out\" multiple straight lines until it found one that minimized the \"loss function\" value for that choice. Then it read off the Intercept and X-slope for that line. \n",
"\n",
"Because this error estimation is an important part of our modeling we're going to take a more detailed look at it. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"We want to create a simple formula for the error or difference between the value of Y given by our straight line, and the actual value of Y from our data set. Unless our line happens to pass through a particular point, this error will be non-zero. It may be positive or negative. We take the square of this error (we can do other things like take the abs value, but here we take the square.....patience, all will be revealed) and then we add up such error terms for each data point to get the total error for this straight line and this data set. \n",
"\n",
"**Important**: for a different set of samples of the **very same** experiment we will get a different data set and possibly a different staright line and so almost certainly a different total error.\n",
"\n",
"The squared error we used is a very commonly used form of the total error previously know as \"quadratic error\". It also has the property that errors in the negative direction and positive direction are treated the same and this \"quadratic error\" or \"square error\" is always have a positive value.\n",
"\n",
"So for now we will use the \"squared error\" as our representation of error. [1]\n",
"\n",
"So Regression in general is any approach we might use to estimate the coefficients of a model using the data to estimate the coefficients by minimizing the \"squared error\". Statistical software uses sophisticated numerical techniques using multivariable calculus to minimize this error and give us estimated values for the coefficients.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Let's try this on some real data.**\n",
"\n",
"We're going to look at a data set of Loan data from [Lending Club](http://www.lendingclub.com), a peer lending web site.\n",
"They have anonymized data on borrowers and loans that have been made. Loan data has many attributes and we'll explore the whole data set in a bit but for now we'll just look at how borrower FICO score affects interest rate charged.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"# we have to clean up the raw data set which we will do\n",
"# in the next lesson. But for now let's look at the cleaned up data.\n",
"# import the cleaned up dataset into a pandas data frame\n",
"df = pd.read_csv('../datasets/loanf.csv')\n",
"\n",
"# extract FICO Score and Interest Rate and plot them\n",
"# FICO Score on x-axis, Interest Rate on y-axis\n",
"intrate = df['Interest.Rate']\n",
"fico = df['FICO.Score']\n",
"p = plot(fico,intrate,'o')\n",
"ax = gca()\n",
"xt = ax.set_xlabel('FICO Score')\n",
"yt = ax.set_ylabel('Interest Rate %')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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t5fE0kJMzVPl+TY+dNaxefSuNjW6cTj/z5uWG9YfWsHz5DgKB5nEsX34v2dny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\nAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we see a distinct downward linear trend where Interest Rate goes down with increasing FICO score. But we also see that for the same FICO score there is a range of Interest rates. This suggests that FICO by itself might not be enough to predict Interest Rate."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multivariate Linear Regression\n",
"\n",
"So the natural question that arises is what happens if Y depends on more than one variable.\n",
"And this is where the power of mathematical generalization comes in. The same principle applies but in multiple dimensions. Not just two or three but much larger numbers. Twenty, thirty or even hundred independent variables are not out of question if we want to model real world data. \n",
"\n",
"But for now let's look at $Y$ as a function of two independent variables, $X_1$ and $X_2$, so \n",
"\n",
"$$ Y = a_0 + a_1X_1 + a_2X_2 $$\n",
"\n",
"Here $a_0$ is the Intercept term and $a_1, a_2$ are the coefficients of $X_1, X_2$, the independent variables respectively. \n",
"\n",
"So to look at a real data set with potentially multiple independent variables we're going to use the Lending Club data set in the next lesson."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"## References\n",
"\n",
"[1] Squared Error http://en.wikipedia.org/wiki/Residual_sum_of_squares"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"../styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<style>\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
" }\n",
" div.cell{\n",
" width:800px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
" }\n",
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} | bsd-2-clause |
sertansenturk/makammusicbrainz | demo.ipynb | 1 | 10508 | {
"cells": [
{
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"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatic pdb calling has been turned ON\n"
]
}
],
"source": [
"import os\n",
"from pprint import pprint\n",
"from makammusicbrainz.audiometadata import AudioMetadata\n",
"from makammusicbrainz.workmetadata import WorkMetadata\n",
"\n",
"%pdb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'artist_credits': [{'mbid': 'f4019a48-bda2-4718-8b51-b2d452b8a716',\n",
" 'name': 'Turkish Ottoman Classical Music Ensemble'}],\n",
" 'artists': [{'attribute-list': ['kanun'],\n",
" 'mbid': '3e753aab-d842-481e-81ea-74918ab1fb04',\n",
" 'name': u'C\\xfcneyt Kosal',\n",
" 'type': 'instrument'},\n",
" {'attribute-list': ['ney'],\n",
" 'mbid': '66f06c81-daec-4530-bd12-5f3b450c1857',\n",
" 'name': u'Ali Do\\u011fan Ergin',\n",
" 'type': 'instrument'},\n",
" {'attribute-list': [u'classical kemen\\xe7e'],\n",
" 'mbid': '92805785-a644-43f7-8bd8-4dd51bf48529',\n",
" 'name': u'C\\xfcneyd Orhon',\n",
" 'type': 'instrument'},\n",
" {'mbid': 'ad21dcd0-0028-4096-81b4-01c20bb179e0',\n",
" 'name': 'Recep Birgit',\n",
" 'type': 'vocal'},\n",
" {'mbid': 'd7e8c73c-66bc-4200-af5e-cac351cdab15',\n",
" 'name': 'Robert Garfias',\n",
" 'type': 'publishing'},\n",
" {'mbid': 'd7e8c73c-66bc-4200-af5e-cac351cdab15',\n",
" 'name': 'Robert Garfias',\n",
" 'type': 'recording'},\n",
" {'attribute-list': ['tanbur'],\n",
" 'mbid': 'eb150720-40d5-4da9-8c8b-b149601c73e5',\n",
" 'name': u'Necdet Ya\\u015far',\n",
" 'type': 'instrument'}],\n",
" 'bit_rate': 56,\n",
" 'duration': 191,\n",
" 'form': [{'attribute_key': u'sarki',\n",
" 'mb_attribute': u'\\u015eark\\u0131',\n",
" 'source': 'http://musicbrainz.org/work/d1451819-125a-489e-987f-66afe227749b'},\n",
" {'attribute_key': u'sarki',\n",
" 'mb_attribute': u'\\u015eark\\u0131',\n",
" 'source': 'http://musicbrainz.org/work/ec05ec50-2ca6-480f-ab65-44390fa6569a'},\n",
" {'attribute_key': u'taksim',\n",
" 'mb_tag': 'taksim',\n",
" 'source': 'http://musicbrainz.org/recording/635530df-8e13-4587-a94d-32f3c1643ca6'}],\n",
" 'instrumentation_voicing': 'Solo vocal with accompaniment',\n",
" 'makam': [{'attribute_key': u'huzzam',\n",
" 'mb_attribute': u'H\\xfczzam',\n",
" 'source': 'http://musicbrainz.org/work/d1451819-125a-489e-987f-66afe227749b'},\n",
" {'attribute_key': u'huzzam',\n",
" 'mb_attribute': u'H\\xfczzam',\n",
" 'source': 'http://musicbrainz.org/work/ec05ec50-2ca6-480f-ab65-44390fa6569a'},\n",
" {'attribute_key': u'huzzam',\n",
" 'mb_tag': u'h\\xfczzam',\n",
" 'source': 'http://musicbrainz.org/recording/635530df-8e13-4587-a94d-32f3c1643ca6'}],\n",
" 'mbid': '635530df-8e13-4587-a94d-32f3c1643ca6',\n",
" 'path': 'sampledata/huzzam_fasil.mp3',\n",
" 'releases': [],\n",
" 'sampling_frequency': 48000,\n",
" 'title': u'H\\xfczzam Fasil',\n",
" 'url': u'http://musicbrainz.org/recording/635530df-8e13-4587-a94d-32f3c1643ca6',\n",
" 'usul': [{'attribute_key': u'aksak',\n",
" 'mb_attribute': 'Aksak',\n",
" 'source': 'http://musicbrainz.org/work/d1451819-125a-489e-987f-66afe227749b'},\n",
" {'attribute_key': u'ciftesofyan',\n",
" 'mb_attribute': u'\\xc7iftesofyan',\n",
" 'source': 'http://musicbrainz.org/work/ec05ec50-2ca6-480f-ab65-44390fa6569a'},\n",
" {'attribute_key': u'serbest',\n",
" 'mb_tag': 'serbest',\n",
" 'source': 'http://musicbrainz.org/recording/635530df-8e13-4587-a94d-32f3c1643ca6'}],\n",
" 'works': [{'mbid': 'd1451819-125a-489e-987f-66afe227749b',\n",
" 'title': u'G\\xf6n\\xfcl Verdim Bir Civane'},\n",
" {'mbid': 'ec05ec50-2ca6-480f-ab65-44390fa6569a',\n",
" 'title': u'Ey G\\xfcl Ba\\u011f-\\u0131 Eda'}]}\n"
]
}
],
"source": [
"# Get audio metadata\n",
"audioMetadata = AudioMetadata(get_work_attributes=True, print_warnings=True)\n",
"\n",
"mp3file = os.path.join('sampledata', 'huzzam_fasil.mp3')\n",
"audio_meta = audioMetadata.from_musicbrainz(mp3file)\n",
"\n",
"pprint(audio_meta)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'composer': {'mbid': '1ab5a8c5-4693-43e4-809d-353990423938',\n",
" 'name': 'Sultan III. Selim'},\n",
" 'form': [{'attribute_key': u'sarki',\n",
" 'mb_attribute': u'\\u015eark\\u0131',\n",
" 'source': 'http://musicbrainz.org/work/d1451819-125a-489e-987f-66afe227749b'}],\n",
" 'language': 'tur',\n",
" 'lyricist': {'mbid': '1ab5a8c5-4693-43e4-809d-353990423938',\n",
" 'name': 'Sultan III. Selim'},\n",
" 'makam': [{'attribute_key': u'huzzam',\n",
" 'mb_attribute': u'H\\xfczzam',\n",
" 'source': 'http://musicbrainz.org/work/d1451819-125a-489e-987f-66afe227749b'}],\n",
" 'mbid': 'd1451819-125a-489e-987f-66afe227749b',\n",
" 'recordings': [{'mbid': '34924045-1f18-4417-9d5d-62135a0407bd',\n",
" 'title': u'H\\xfczzam Fas\\u0131l'},\n",
" {'mbid': '470c6b30-22dd-465e-b695-14aa594c2426',\n",
" 'title': u'H\\xfczzam \\u015eark\\u0131'},\n",
" {'mbid': '5f4ea98f-66fd-4991-87b2-eabdce26ab66',\n",
" 'title': u'G\\xf6n\\xfcl Verdim Bir Civane'},\n",
" {'mbid': '635530df-8e13-4587-a94d-32f3c1643ca6',\n",
" 'title': u'H\\xfczzam Fasil'}],\n",
" 'scores': [u'huzzam--sarki--aksak--gonul_verdim--iii_selim'],\n",
" 'title': u'G\\xf6n\\xfcl Verdim Bir Civane',\n",
" 'url': 'http://musicbrainz.org/work/d1451819-125a-489e-987f-66afe227749b',\n",
" 'usul': [{'attribute_key': u'aksak',\n",
" 'mb_attribute': 'Aksak',\n",
" 'source': 'http://musicbrainz.org/work/d1451819-125a-489e-987f-66afe227749b'}]},\n",
" {'composer': {'mbid': '81081f45-a36e-4248-9d33-43d4a3445f6d',\n",
" 'name': 'Dede Efendi'},\n",
" 'form': [{'attribute_key': u'sarki',\n",
" 'mb_attribute': u'\\u015eark\\u0131',\n",
" 'source': 'http://musicbrainz.org/work/ec05ec50-2ca6-480f-ab65-44390fa6569a'}],\n",
" 'language': 'ota',\n",
" 'lyricist': {'mbid': '125ec42a-7229-4250-afc5-e057484327fe',\n",
" 'name': '[unknown]'},\n",
" 'makam': [{'attribute_key': u'huzzam',\n",
" 'mb_attribute': u'H\\xfczzam',\n",
" 'source': 'http://musicbrainz.org/work/ec05ec50-2ca6-480f-ab65-44390fa6569a'}],\n",
" 'mbid': 'ec05ec50-2ca6-480f-ab65-44390fa6569a',\n",
" 'recordings': [{'mbid': '34924045-1f18-4417-9d5d-62135a0407bd',\n",
" 'title': u'H\\xfczzam Fas\\u0131l'},\n",
" {'mbid': '3799145c-529c-41d2-998a-948a9292f4d8',\n",
" 'title': u'Ey G\\xfcl-i Ba\\u011f-\\u0131 Eda'},\n",
" {'mbid': '5b26b215-c190-4893-92a2-8e51b6f90ce7',\n",
" 'title': u'H\\xfczzam \\u015eark\\u0131 / Ey G\\xfcl-i Ba\\u011f-\\u0131 Eda'},\n",
" {'mbid': '635530df-8e13-4587-a94d-32f3c1643ca6',\n",
" 'title': u'H\\xfczzam Fasil'},\n",
" {'mbid': '83ae9b76-c51d-4fb7-bcaa-1d02df1a8247',\n",
" 'title': u'Ey G\\xfcl Ba\\u011f-\\u0131 Eda'},\n",
" {'mbid': 'c6089405-6f18-4562-8c24-fff2adc42eee',\n",
" 'title': u'Ey G\\xfcl-i B\\xe2\\u011f-\\u0131 Ed\\xe2 (H\\xfczzam \\u015eark\\u0131)'},\n",
" {'mbid': 'd4ac070b-ff98-40d9-9bd9-cb7f5cb25e2f',\n",
" 'title': u'Ey G\\xfcl-i Ba\\u011f-\\u0131 Eda Sana Oldum M\\xfcptela'},\n",
" {'mbid': 'de6303ac-c45c-44fc-a91d-fd58c01b4aa5',\n",
" 'title': u'Ey G\\xfcli Ba\\u011f\\u0131 Eda'}],\n",
" 'scores': [u'huzzam--sarki--ciftesofyan--ey_gul-i--dede_efendi'],\n",
" 'title': u'Ey G\\xfcl Ba\\u011f-\\u0131 Eda',\n",
" 'url': 'http://musicbrainz.org/work/ec05ec50-2ca6-480f-ab65-44390fa6569a',\n",
" 'usul': [{'attribute_key': u'ciftesofyan',\n",
" 'mb_attribute': u'\\xc7iftesofyan',\n",
" 'source': 'http://musicbrainz.org/work/ec05ec50-2ca6-480f-ab65-44390fa6569a'}]}]\n"
]
}
],
"source": [
"# get work metadata\n",
"workMetadata = WorkMetadata(get_recording_rels=True, print_warnings=True)\n",
"\n",
"work_meta = []\n",
"for w in audio_meta['works']:\n",
" work_meta.append(workMetadata.from_musicbrainz(w['mbid']))\n",
"\n",
"pprint(work_meta)\n"
]
}
],
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pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/Expressions.ipynb | 1 | 29238 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Expressions\n",
"\n",
"Rational expressions, or _expressions_ for short, denote (rational) languages in a compact way. Since Vcsn supports weighted expressions, they actually can denoted rational series.\n",
"\n",
"This page documents the syntax and transformations (called _identities_) that are applied to every expression. The list of available algorithms using expression is in the [Algorithms page](Algorithms.ipynb).\n",
"\n",
"## Syntax\n",
"The syntax for rational expressions is as follows (with increasing precedence):\n",
"- `\\z`, the empty expression.\n",
"- `\\e`, the empty word.\n",
"- `a`, the letter `a`.\n",
"- `'l'`, the label `l` (useful, for instance, when labels are words, or to denote a letter which is an operator: `'+'` denotes the `+` letter).\n",
"- `[abcd]`, letter class, equivalent to `(a+b+c+d)`.\n",
"- `[a-d]`, one letter _of the current alphabet_ between `a` and `d`. If the alphabet is $\\{a, d, e\\}$, `[a-d]` denotes `[ad]`, not `[abcd]`.\n",
"- `[^a-dz]`, one letter of the current alphabet that is not part of `[a-dz]`.\n",
"- `[^]`, any letter of the current alphabet (\"any letter other that none\").\n",
"- `(e)`, `e`.\n",
"- `e+f`, the [addition](expression.sum.ipynb) (disjunction, union) of `e` and `f` (note the use of `+`, `|` is not accepted).\n",
"- `e&f`, the [conjunction](expression.conjunction.ipynb) (intersection) of `e` and `f`.\n",
"- `e:f`, the [shuffle product](expression.shuffle.ipynb) (interleaving) of `e` and `f`.\n",
"- `e&:f`, the [infiltration](expression.infiltration.ipynb) of `e` and `f`.\n",
"- `ef` and `e.f`, the [multiplication](expression.multiplication.ipynb) (concatenation) of `e` and `f`.\n",
"- `<k>e`, the left exterior product (left-scalar product) of `e` by `k`.\n",
"- `e<k>`, the right exterior product (right-scalar product) of `e` by `k`.\n",
"- `e*` and `e{*}`, any number of repetitions of `e` (the Kleene closure of `e`).\n",
"- [`e{n}`](expression.multiplication.ipynb), the power (repeated multiplication) of `e` `n` times: `ee ... e`.\n",
"- [`e{n,m}`](expression.multiplication.ipynb), any repetition of `e` between `n` and `m`, i.e., the sum of the powers of `e` between `n` and `m`: `e{n}+e{n+1}+ ... +e{m}`.\n",
"- [`e{n,}`](expression.multiplication.ipynb), the sum of powers of `e` at least `n` times: `e{n}e*`.\n",
"- [`e{,m}`](expression.multiplication.ipynb), at most `m` repetitions of `e`: `e{0,m}`.\n",
"- [`e{+}`](expression.multiplication.ipynb), at least one `e`: `e{1,}`.\n",
"- [`e?`](expression.multiplication.ipynb), `e{?}`, `e` optional: `e{0,1}`.\n",
"- `e{c}`, the [complement](expression.complement.ipynb) of `e`.\n",
"\n",
"where `e` and `f` denote expressions, `a` a label, `k` a weight, and `n` and `m` natural numbers.\n",
"\n",
"Please note that contrary to \"regexps\" (as in grep, perl, etc.):\n",
"- spaces are ignored\n",
"- `+` denotes the choice, not `|`\n",
"- `.` denotes the concatenation, use `[^]` to mean \"any letter\"\n",
"\n",
"## Identities\n",
"Some rewriting rules are applied on the expressions to \"simplify\" them. The strength of this simplification is graduated.\n",
"\n",
"- `none`: no identities at all. Some algorithms, such as `derived_term`, might not terminate.\n",
"- `trivial`: the trivial identities only are applied.\n",
"- `associative`: the associative identities are added.\n",
"- `linear`: the linear identities are added. This is the default.\n",
"- `distributive`: the distributive identities are added.\n",
"\n",
"### Trivial Identities\n",
"\n",
"$$\n",
"\\newcommand{\\eword}{\\varepsilon}\n",
"\\newcommand{\\lmul}[2]{\\bra{#1}{#2}}\n",
"\\newcommand{\\rmul}[2]{#1\\bra{#2}}\n",
"\\newcommand{\\lmulq}[2]{\\bra{#1}^?{#2}}\n",
"\\newcommand{\\rmulq}[2]{#1\\bra{#2}^?}\n",
"\\newcommand{\\bra}[1]{\\langle#1\\rangle}\n",
"\\newcommand{\\K}{\\mathbb{K}}\n",
"\\newcommand{\\zed}{\\mathsf{0}}\n",
"\\newcommand{\\und}{\\mathsf{1}}\n",
"\\newcommand{\\zeK}{0_{\\K}}\n",
"\\newcommand{\\unK}{1_{\\K}}\n",
"\\newcommand{\\Ed}{\\mathsf{E}}\n",
"\\newcommand{\\Fd}{\\mathsf{F}}\n",
"\\newcommand{\\Gd}{\\mathsf{G}}\n",
"\\begin{gather*}\n",
"% \\tag{add}\n",
" \\Ed+\\zed \\Rightarrow \\Ed\n",
" \\quad\n",
" \\zed+\\Ed \\Rightarrow \\Ed\n",
" \\\\[.7ex] %\\tag{kmul}\n",
" \\begin{aligned}[t]\n",
" \\lmul{\\zeK}{\\Ed} & \\Rightarrow \\zed &\n",
" \\lmul{\\unK}{\\Ed} & \\Rightarrow \\Ed &\n",
" \\lmul{k}{\\zed} & \\Rightarrow \\zed &\n",
" \\lmul{k}{\\lmul{h}{\\Ed}} &\\Rightarrow \\lmul{kh}{\\Ed}\n",
" \\\\\n",
" \\rmul{\\Ed}{\\zeK} & \\Rightarrow \\zed &\n",
" \\rmul{\\Ed}{\\unK} & \\Rightarrow \\Ed &\n",
" \\rmul{\\zed}{k} & \\Rightarrow \\zed &\n",
" \\rmul{\\rmul{\\Ed}{k}}{h} &\\Rightarrow \\rmul{\\Ed}{kh}\n",
" \\end{aligned}\\\\\n",
" \\rmul{(\\lmul{k}{\\Ed})}{h} \\Rightarrow \\lmul{k}{(\\rmul{\\Ed}{h})} \\quad\n",
" \\rmul{\\ell}{k} \\Rightarrow \\lmul{k}{\\ell}\n",
" \\\\ %\\tag{mul}\n",
" \\Ed \\cdot \\zed \\Rightarrow \\zed \\quad\n",
" \\zed \\cdot \\Ed \\Rightarrow \\zed\n",
" \\\\\n",
" (\\lmulq{k}{\\und}) \\cdot \\Ed \\Rightarrow \\lmulq{k}{\\Ed}\n",
" \\quad\n",
" \\Ed \\cdot (\\lmulq{k}{\\und}) \\Rightarrow \\rmulq{\\Ed}{k}\n",
" \\\\ %\\tag{star}\n",
" \\zed^\\star \\Rightarrow \\und\n",
" \\\\\n",
" \\zed^c \\& \\Ed \\Rightarrow \\Ed\n",
" \\quad\n",
" \\Ed \\& \\zed^c \\Rightarrow \\Ed\n",
" \\\\\n",
" (\\lmul{k}{\\Ed})^{c} \\Rightarrow \\Ed^{c} \\quad (\\rmul{\\Ed}{k})^{c} \\Rightarrow \\Ed^{c}\n",
" \\\\\n",
" {\\Ed^c}^c \\Rightarrow \\Ed \\text{ if the weights are Boolean ($\\mathbb{B}$ or $\\mathbb{F}_2$)}\n",
"\\end{gather*}\n",
"$$\n",
"\n",
"where $\\Ed$ stands for any rational expression, $a \\in A$~is any letter,\n",
"$\\ell\\in A \\cup \\{\\eword\\}$, $k, h\\in \\K$ are weights, and $\\lmulq{k}{\\ell}$\n",
"denotes either $\\lmul{k}{\\ell}$, or $\\ell$ in which case $k = \\unK$ in the\n",
"right-hand side. Any subexpression of a form listed to the left of a\n",
"'$\\Rightarrow$' is rewritten as indicated on the right.\n",
"\n",
"### Associative Identities\n",
"In addition to the trivial identities, the binary operators (addition, conjunction, multiplication) are made associative. Actually, they become variadic instead of being strictly binary.\n",
"$$\n",
"\\begin{align}\n",
" \\Ed+(\\Fd+\\Gd) & \\Rightarrow \\Ed+\\Fd+\\Gd\\\\\n",
" \\Ed(\\Fd\\Gd) & \\Rightarrow \\Ed\\Fd\\Gd\\\\\n",
" \\Ed\\&(\\Fd\\&\\Gd) & \\Rightarrow \\Ed\\&\\Fd\\&\\Gd\\\\\n",
"\\end{align}\n",
"$$\n",
"\n",
"### Linear Identities\n",
"In addition to the associative identities, the addition is made commutative. Actually, members of sums are now sorted, and weights of equal terms are added. Some identities requires the weightset to be a commutative semiring (i.e., the product of scalars is commutative).\n",
"$$\n",
"\\begin{align}\n",
" \\Fd+\\Ed & \\Rightarrow \\Ed+\\Fd && \\text{if $\\Ed < \\Fd$} \\\\\n",
" \\lmul{k}{\\Ed}+\\lmul{h}{\\Ed} & \\Rightarrow \\lmul{k+h}{\\Ed}\\\\\n",
" \\rmul{\\Ed}{k} & \\Rightarrow \\lmul{k}{\\Ed} && \\text{if commutative} \\\\\n",
" \\lmul{k}{\\Ed}\\lmul{h}{\\Fd} & \\Rightarrow \\lmul{kh}{(\\Ed\\Fd)} && \\text{if commutative} \\\\\n",
"\\end{align}\n",
"$$\n",
"\n",
"### Distributive Identities\n",
"In addition to the linear identities, the multiplication and multiplication by a scalar are distributed on the sum.\n",
"$$\n",
"\\begin{gather*}\n",
" \\lmul{k}{(\\Ed+\\Fd)} \\Rightarrow \\lmul{k}{\\Ed} + \\lmul{k}{\\Fd} \\\\\n",
" \\Ed(\\Fd+\\Gd) \\Rightarrow \\Ed\\Fd + \\Ed\\Gd \\qquad\n",
" (\\Ed+\\Fd)\\Gd \\Rightarrow \\Ed\\Gd + \\Fd\\Gd \\\\\n",
"\\end{gather*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examples"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import vcsn\n",
"import pandas as pd\n",
"pd.options.display.max_colwidth = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following helping routine takes a list of expressions as text (`*es`), converts them into genuine expression objects (`ctx.expression(e, id)`) for each `id`, formats them into LaTeX, and puts them in a DataFrame for display."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ids = ['trivial', 'associative', 'linear', 'distributive']\n",
"ctx = vcsn.context('lal_char(a-z), b')\n",
"def example(*es):\n",
" res = []\n",
" for e in es:\n",
" res.append([e] + ['$' + ctx.expression(e, id).format('latex') + '$' for id in ids])\n",
" return pd.DataFrame(res, columns=['Input'] + list(map(str.title, ids)))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Input</th>\n",
" <th>Trivial</th>\n",
" <th>Associative</th>\n",
" <th>Linear</th>\n",
" <th>Distributive</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>$a$</td>\n",
" <td>$a$</td>\n",
" <td>$a$</td>\n",
" <td>$a$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>a+b+c</td>\n",
" <td>$\\left(a + b\\right) + c$</td>\n",
" <td>$a + b + c$</td>\n",
" <td>$a + b + c$</td>\n",
" <td>$a \\oplus b \\oplus c$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>a+(b+c)</td>\n",
" <td>$a + \\left(b + c\\right)$</td>\n",
" <td>$a + b + c$</td>\n",
" <td>$a + b + c$</td>\n",
" <td>$a \\oplus b \\oplus c$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>a+b+c+d</td>\n",
" <td>$\\left(\\left(a + b\\right) + c\\right) + d$</td>\n",
" <td>$[a\\textrm{-}d]$</td>\n",
" <td>$[a\\textrm{-}d]$</td>\n",
" <td>$[a\\textrm{-}d]$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>b+a</td>\n",
" <td>$b + a$</td>\n",
" <td>$b + a$</td>\n",
" <td>$a + b$</td>\n",
" <td>$a \\oplus b$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>[ab][ab]</td>\n",
" <td>$\\left(a + b\\right) \\, \\left(a + b\\right)$</td>\n",
" <td>$\\left(a + b\\right) \\, \\left(a + b\\right)$</td>\n",
" <td>$\\left(a + b\\right) \\, \\left(a + b\\right)$</td>\n",
" <td>$a \\, a \\oplus a \\, b \\oplus b \\, a \\oplus b \\, b$</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Input Trivial \\\n",
"0 a $a$ \n",
"1 a+b+c $\\left(a + b\\right) + c$ \n",
"2 a+(b+c) $a + \\left(b + c\\right)$ \n",
"3 a+b+c+d $\\left(\\left(a + b\\right) + c\\right) + d$ \n",
"4 b+a $b + a$ \n",
"5 [ab][ab] $\\left(a + b\\right) \\, \\left(a + b\\right)$ \n",
"\n",
" Associative \\\n",
"0 $a$ \n",
"1 $a + b + c$ \n",
"2 $a + b + c$ \n",
"3 $[a\\textrm{-}d]$ \n",
"4 $b + a$ \n",
"5 $\\left(a + b\\right) \\, \\left(a + b\\right)$ \n",
"\n",
" Linear \\\n",
"0 $a$ \n",
"1 $a + b + c$ \n",
"2 $a + b + c$ \n",
"3 $[a\\textrm{-}d]$ \n",
"4 $a + b$ \n",
"5 $\\left(a + b\\right) \\, \\left(a + b\\right)$ \n",
"\n",
" Distributive \n",
"0 $a$ \n",
"1 $a \\oplus b \\oplus c$ \n",
"2 $a \\oplus b \\oplus c$ \n",
"3 $[a\\textrm{-}d]$ \n",
"4 $a \\oplus b$ \n",
"5 $a \\, a \\oplus a \\, b \\oplus b \\, a \\oplus b \\, b$ "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example('a', 'a+b+c', 'a+(b+c)', 'a+b+c+d', 'b+a', '[ab][ab]')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A few more examples, with weights in $\\mathbb{Q}$:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Input</th>\n",
" <th>Trivial</th>\n",
" <th>Associative</th>\n",
" <th>Linear</th>\n",
" <th>Distributive</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>$a$</td>\n",
" <td>$a$</td>\n",
" <td>$a$</td>\n",
" <td>$a$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>a+a+a</td>\n",
" <td>$\\left(a + a\\right) + a$</td>\n",
" <td>$a + a + a$</td>\n",
" <td>$ \\left\\langle 3 \\right\\rangle \\,a$</td>\n",
" <td>$ \\left\\langle 3 \\right\\rangle \\,a$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>a+a+b</td>\n",
" <td>$\\left(a + a\\right) + b$</td>\n",
" <td>$a + a + b$</td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,a + b$</td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,a \\oplus b$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>a+b+a</td>\n",
" <td>$\\left(a + b\\right) + a$</td>\n",
" <td>$a + b + a$</td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,a + b$</td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,a \\oplus b$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td><2>(a+b)</td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,\\left(a + b\\right)$</td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,\\left(a + b\\right)$</td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,\\left(a + b\\right)$</td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,a \\oplus \\left\\langle 2 \\right\\rangle \\,b$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>([ab]+[ab]){2}</td>\n",
" <td>$\\left(\\left(a + b\\right) + \\left(a + b\\right)\\right) \\, \\left(\\left(a + b\\right) + \\left(a + b\\right)\\right)$</td>\n",
" <td>$\\left(a + b + a + b\\right) \\, \\left(a + b + a + b\\right)$</td>\n",
" <td>$\\left( \\left\\langle 2 \\right\\rangle \\,a + \\left\\langle 2 \\right\\rangle \\,b\\right) \\, \\left( \\left\\langle 2 \\right\\rangle \\,a + \\left\\langle 2 \\right\\rangle \\,b\\right)$</td>\n",
" <td>$ \\left\\langle 4 \\right\\rangle \\,\\left(a \\, a\\right) \\oplus \\left\\langle 4 \\right\\rangle \\,\\left(a \\, b\\right) \\oplus \\left\\langle 4 \\right\\rangle \\,\\left(b \\, a\\right) \\oplus \\left\\langle 4 \\right\\rangle \\,\\left(b \\, b\\right)$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td><2>ab<3>cd<5></td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,a \\, \\left(b \\, \\left( \\left\\langle 3 \\right\\rangle \\,c \\, \\left\\langle 5 \\right\\rangle \\,d\\right)\\right)$</td>\n",
" <td>$ \\left\\langle 2 \\right\\rangle \\,a \\, b \\, \\left\\langle 3 \\right\\rangle \\,c \\, \\left\\langle 5 \\right\\rangle \\,d$</td>\n",
" <td>$ \\left\\langle 30 \\right\\rangle \\,\\left(a \\, b \\, c \\, d\\right)$</td>\n",
" <td>$ \\left\\langle 30 \\right\\rangle \\,\\left(a \\, b \\, c \\, d\\right)$</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Input \\\n",
"0 a \n",
"1 a+a+a \n",
"2 a+a+b \n",
"3 a+b+a \n",
"4 <2>(a+b) \n",
"5 ([ab]+[ab]){2} \n",
"6 <2>ab<3>cd<5> \n",
"\n",
" Trivial \\\n",
"0 $a$ \n",
"1 $\\left(a + a\\right) + a$ \n",
"2 $\\left(a + a\\right) + b$ \n",
"3 $\\left(a + b\\right) + a$ \n",
"4 $ \\left\\langle 2 \\right\\rangle \\,\\left(a + b\\right)$ \n",
"5 $\\left(\\left(a + b\\right) + \\left(a + b\\right)\\right) \\, \\left(\\left(a + b\\right) + \\left(a + b\\right)\\right)$ \n",
"6 $ \\left\\langle 2 \\right\\rangle \\,a \\, \\left(b \\, \\left( \\left\\langle 3 \\right\\rangle \\,c \\, \\left\\langle 5 \\right\\rangle \\,d\\right)\\right)$ \n",
"\n",
" Associative \\\n",
"0 $a$ \n",
"1 $a + a + a$ \n",
"2 $a + a + b$ \n",
"3 $a + b + a$ \n",
"4 $ \\left\\langle 2 \\right\\rangle \\,\\left(a + b\\right)$ \n",
"5 $\\left(a + b + a + b\\right) \\, \\left(a + b + a + b\\right)$ \n",
"6 $ \\left\\langle 2 \\right\\rangle \\,a \\, b \\, \\left\\langle 3 \\right\\rangle \\,c \\, \\left\\langle 5 \\right\\rangle \\,d$ \n",
"\n",
" Linear \\\n",
"0 $a$ \n",
"1 $ \\left\\langle 3 \\right\\rangle \\,a$ \n",
"2 $ \\left\\langle 2 \\right\\rangle \\,a + b$ \n",
"3 $ \\left\\langle 2 \\right\\rangle \\,a + b$ \n",
"4 $ \\left\\langle 2 \\right\\rangle \\,\\left(a + b\\right)$ \n",
"5 $\\left( \\left\\langle 2 \\right\\rangle \\,a + \\left\\langle 2 \\right\\rangle \\,b\\right) \\, \\left( \\left\\langle 2 \\right\\rangle \\,a + \\left\\langle 2 \\right\\rangle \\,b\\right)$ \n",
"6 $ \\left\\langle 30 \\right\\rangle \\,\\left(a \\, b \\, c \\, d\\right)$ \n",
"\n",
" Distributive \n",
"0 $a$ \n",
"1 $ \\left\\langle 3 \\right\\rangle \\,a$ \n",
"2 $ \\left\\langle 2 \\right\\rangle \\,a \\oplus b$ \n",
"3 $ \\left\\langle 2 \\right\\rangle \\,a \\oplus b$ \n",
"4 $ \\left\\langle 2 \\right\\rangle \\,a \\oplus \\left\\langle 2 \\right\\rangle \\,b$ \n",
"5 $ \\left\\langle 4 \\right\\rangle \\,\\left(a \\, a\\right) \\oplus \\left\\langle 4 \\right\\rangle \\,\\left(a \\, b\\right) \\oplus \\left\\langle 4 \\right\\rangle \\,\\left(b \\, a\\right) \\oplus \\left\\langle 4 \\right\\rangle \\,\\left(b \\, b\\right)$ \n",
"6 $ \\left\\langle 30 \\right\\rangle \\,\\left(a \\, b \\, c \\, d\\right)$ "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ctx = vcsn.Q\n",
"example('a', 'a+a+a', 'a+a+b', 'a+b+a', '<2>(a+b)', '([ab]+[ab]){2}', '<2>ab<3>cd<5>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Try it!\n",
"The following piece of code defines an interactive function for you to try your own expression. Enter an expression in the text area, then click on the \"Run\" button."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Input</th>\n",
" <th>Trivial</th>\n",
" <th>Associative</th>\n",
" <th>Linear</th>\n",
" <th>Distributive</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[ab]{3,}</td>\n",
" <td>$\\left(\\left(\\left(a + b\\right) \\, \\left(a + b\\right)\\right) \\, \\left(a + b\\right)\\right) \\, \\left(a + b\\right)^{*}$</td>\n",
" <td>$\\left(a + b\\right) \\, \\left(a + b\\right) \\, \\left(a + b\\right) \\, \\left(a + b\\right)^{*}$</td>\n",
" <td>$\\left(a + b\\right) \\, \\left(a + b\\right) \\, \\left(a + b\\right) \\, \\left(a + b\\right)^{*}$</td>\n",
" <td>$a \\, a \\, a \\, \\left(a \\oplus b\\right)^{*} \\oplus a \\, a \\, b \\, \\left(a \\oplus b\\right)^{*} \\oplus a \\, b \\, a \\, \\left(a \\oplus b\\right)^{*} \\oplus a \\, b \\, b \\, \\left(a \\oplus b\\right)^{*} \\oplus b \\, a \\, a \\, \\left(a \\oplus b\\right)^{*} \\oplus b \\, a \\, b \\, \\left(a \\oplus b\\right)^{*} \\oplus b \\, b \\, a \\, \\left(a \\oplus b\\right)^{*} \\oplus b \\, b \\, b \\, \\left(a \\oplus b\\right)^{*}$</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Input \\\n",
"0 [ab]{3,} \n",
"\n",
" Trivial \\\n",
"0 $\\left(\\left(\\left(a + b\\right) \\, \\left(a + b\\right)\\right) \\, \\left(a + b\\right)\\right) \\, \\left(a + b\\right)^{*}$ \n",
"\n",
" Associative \\\n",
"0 $\\left(a + b\\right) \\, \\left(a + b\\right) \\, \\left(a + b\\right) \\, \\left(a + b\\right)^{*}$ \n",
"\n",
" Linear \\\n",
"0 $\\left(a + b\\right) \\, \\left(a + b\\right) \\, \\left(a + b\\right) \\, \\left(a + b\\right)^{*}$ \n",
"\n",
" Distributive \n",
"0 $a \\, a \\, a \\, \\left(a \\oplus b\\right)^{*} \\oplus a \\, a \\, b \\, \\left(a \\oplus b\\right)^{*} \\oplus a \\, b \\, a \\, \\left(a \\oplus b\\right)^{*} \\oplus a \\, b \\, b \\, \\left(a \\oplus b\\right)^{*} \\oplus b \\, a \\, a \\, \\left(a \\oplus b\\right)^{*} \\oplus b \\, a \\, b \\, \\left(a \\oplus b\\right)^{*} \\oplus b \\, b \\, a \\, \\left(a \\oplus b\\right)^{*} \\oplus b \\, b \\, b \\, \\left(a \\oplus b\\right)^{*}$ "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ipywidgets import interact_manual\n",
"from IPython.display import display\n",
"es = []\n",
"@interact_manual\n",
"def interactive_example(expression = \"[ab]{3,}\"):\n",
" es.append(expression)\n",
" display(example(*es))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
gfrias/udacity | 4_advanced_lines/project.ipynb | 1 | 75880419 | null | mit |
ireapps/cfj-2017 | exercises/17. Miscellaneous-working.ipynb | 1 | 7962 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Miscellaneous Python things\n",
"\n",
"In this session, we'll talk about:\n",
"\n",
"- More control flow tools: [`try/except`](https://docs.python.org/3/tutorial/errors.html), [`break`](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement) and [`continue`](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement)\n",
"- A few other built-in Python functions\n",
"- Installing third-party modules"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Handling errors with try/except\n",
"\n",
"Sometimes your script will throw errors. When it does, sometimes you want the script to continue after handling the error in some way. Let's take a look at some examples.\n",
"\n",
"What happens when we run the code below?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"humans = [\n",
" {'name': 'Cody', 'age': 32, 'job': 'Training director', 'height_in': 72},\n",
" {'name': 'Jeff', 'age': 44, 'job': 'Snake charmer', 'height_in': 60},\n",
" {'name': 'Sally', 'age': 55, 'job': 'Fry cook'}\n",
"]\n",
"\n",
"for human in humans:\n",
" print(human['name'], 'is', human['height_in'], 'inches tall')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's catch the `KeyError`. You could use a bare `except` statement, which would fire if _any_ exception is raised, but it's good practice to specify the class of error that you're controlling for."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"humans = [\n",
" {'name': 'Cody', 'age': 32, 'job': 'Training director', 'height_in': 72},\n",
" {'name': 'Jeff', 'age': 44, 'job': 'Snake charmer', 'height_in': 60},\n",
" {'name': 'Sally', 'age': 55, 'job': 'Fry cook'}\n",
"]\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Break and continue\n",
"\n",
"These statements are frequently used in loops to control the flow of your program. We'll use [`range()`](https://docs.python.org/3/library/functions.html#func-range) to demo how each statement works.\n",
"\n",
"- [`break`](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement) breaks out of the loop\n",
"- [`continue`](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement) skips to the next iteration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# break\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# continue\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Other built-in functions\n",
"\n",
"Check out the [full list here](https://docs.python.org/3/library/functions.html#built-in-functions). We're just going to look at a couple."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### [`dir()`](https://docs.python.org/3/library/functions.html#dir)\n",
"\n",
"Use the `dir()` function to see all of the attributes and methods available to an object -- this is often how I learn about new ways to manipulate data!\n",
"\n",
"Let's try it out on some different data types."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# string\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# lists\n",
"l = [1, 2, 3, 4, 5, 6]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# dicts\n",
"d = {'name': 'Cody', 'age': 32, 'job': 'Training director', 'height_in': 72}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### [`enumerate()`](https://docs.python.org/3/library/functions.html#enumerate)\n",
"\n",
"Use `enumerate()` in a loop to keep track of _where_ you're at in the loop -- the index. Notice that we then need to use two variables in the loop -- the index and the actual value."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"humans = [\n",
" {'name': 'Cody', 'age': 32, 'job': 'Training director', 'height_in': 72},\n",
" {'name': 'Jeff', 'age': 44, 'job': 'Snake charmer', 'height_in': 60},\n",
" {'name': 'Sally', 'age': 55, 'job': 'Fry cook'}\n",
"]\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### [`zip()`](https://docs.python.org/3/library/functions.html#zip) and [`dict()`](https://docs.python.org/3/library/functions.html#func-dict)\n",
"\n",
"Use zip to fold multiple iterable objects into one thing. My favorite use of zip is turning two lists of related data into a single dictionary using `dict()` to coerce the zip object:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"names = ['Cody', 'Jeff', 'Sally']\n",
"ages = [32, 44, 55]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### [`sum()`](https://docs.python.org/3/library/functions.html#sum), [`max()`](https://docs.python.org/3/library/functions.html#max) and [`min()`](https://docs.python.org/3/library/functions.html#min)\n",
"\n",
"- Sum a list of numbers\n",
"- Find the highest value in a list\n",
"- Find the lowest value in a list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# a list of numbers\n",
"l = [1100, 200, 9400, 800, 1000]\n",
"\n",
"# sum\n",
"\n",
"\n",
"# max\n",
"\n",
"\n",
"# min\n",
"\n",
"\n",
"# print the results\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installing third-party packages\n",
"\n",
"Use the `pip` package manager to install third-party packages. The `pip` tool comes bundled with Python 3. To install a module system-wide, you'd run this command from the Terminal app: `pip install name_of_your_package`.\n",
"\n",
"A saner approach would be to use a \"virtual environment\" and install dependencies specifically for each project. That way, you avoid the problem of \"I need version X of `pandas` for this project but version Y for this other project.\" If `pandas` is installed globally on your computer, you'll quickly run into problems.\n",
"\n",
"For this boot camp, we have installed these packages:\n",
"\n",
"- `jupyter`\n",
"- `bs4` (Beautiful Soup)\n",
"- `requests`\n",
"- `pandas`\n",
"- `matplotlib`\n",
"\n",
"Each of these packages, in turn, has dependencies that are automatically installed."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
shiquan-lim/y3optim | notebooks/.ipynb_checkpoints/Model Performance-checkpoint.ipynb | 1 | 1427596 | null | apache-2.0 |
olgabot/cshl-singlecell-2017 | notebooks/in_progress/02_tissue_subpopulations/05_pca_vs_ica.ipynb | 1 | 3109101 | null | mit |
networks-lab/isilib | notebooks/Lesson-1-Getting-Started/Getting-Started.ipynb | 1 | 3392 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# About Jupyter Notebooks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This document was made from a [jupyter](https://jupyter.org) notebook and can show and run python code. The document is broken up into what are called cells, each cell is either code, output, or markdown (text). For example this cell is markdown, which means it is plain text with a couple small formatting things, like the link in the first sentence. You can change the cell type using the dropdown menu at the top of the page.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is an output cell\n"
]
}
],
"source": [
"#This cell is python\n",
"#The cell below it is output\n",
"print(\"This is an output cell\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code cells contain python code that you can edit and run your self. Try changing the one above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First you need to import the _metaknowledge_ package\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import metaknowledge as mk"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And you will often need the [_networkx_](https://networkx.github.io/documentation/networkx-1.9.1/) package\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import networkx as nx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And [_matplotlib_](http://matplotlib.org/) to display the graphs and to make them look nice when displayed\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_metaknowledge_ also has a _matplotlib_ based graph [visualizer](http://networkslab.org/metaknowledge/docs/visual#visual) that will be used sometimes\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import metaknowledge.visual as mkv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These lines of code will be at the top of all the other lessons as they are what let us use _metaknowledge_."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
nonabelian/tda_dionysus | TADA_Dionysus.ipynb | 1 | 552330 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dionysus and Topological Data in Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A basic, fast-paced, and hopefully understandable introduction to ideas and applications of topological data analysis (TDA) using *Dionysus* in Python."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Topological Data Analysis main points, extremely informally:\n",
"* Topology is classes of surfaces continuously deformable into each other.\n",
"* Surface is infinitely stretchy and compressible, but no ripping of the surface allowed.\n",
"* Topological data is a discretization of ideas from topology.\n",
"* Provides access to invariants (and more) under deformation.\n",
"* Data has shape, and shape has meaning.\n",
"* Difficult to understand high-dimensional (>3) space.\n",
"\n",
"Famously, \"the coffee cup is topologically equivalent to a donut\".\n",
"\n",
"**Think of rescaling features as a deformation**"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n",
"# Local companion package\n",
"from topology.data import coffee_mug\n",
"from topology.data import pail\n",
"from topology.plotting import plot_mug_3D\n",
"from topology.plotting import plot_pail_3D\n",
"from topology.plotting import plot_circle_2D\n",
"\n",
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"images/Mug_and_Torus_morph.gif\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image\n",
"from IPython.display import display\n",
"#https://en.wikipedia.org/wiki/File:Mug_and_Torus_morph.gif\n",
"display(Image(url=\"images/Mug_and_Torus_morph.gif\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Dionysus** is a package for analyzing the topology (think holes, circles, handles, and the higher dimensional analogues) of data.\n",
"\n",
"* PRO: It's one of the few TDA options out there.\n",
"* PRO: Accessible through python bindings.\n",
"* PRO: Provides access to quite a few features.\n",
"* CON: Not very well documented.\n",
"* CON: Not completely accessible through Python.\n",
"* CON: Code is difficult to read and navigate. (Especially C++ code for non-specialists).\n",
"\n",
"It uses \"persistent homology\":\n",
"\n",
"* Connect points at each length scale from a range of scales.\n",
"* Persistence: Feature stays over many length scales -- more important.\n",
"* Add points, then add lines, then eventually circles, ..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## By Example"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x125101850>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_circle_2D()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
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pa92Fjpe7gWEqfCGvyMSFcu8iH7F9mZsGWRo46AQgBrK4wd1sJUQVj6vUMjt4H8ZCHkJWaBQ5rIoIAoIAMocCKSAAGAg+eTkXKwAW+HYz4/98Xi7MkAf4A8AB8sf+wDyki93HeVQF1LEMBQCAgGKDIT4FwfDoK4fIFsYcb5qbURd5ghkgcJcPBml6aKADwUEAH2IJqf8TwEBQJkNyagAgIrTuqD/bPVj/z9E/eKAxQ+WPwSBIAEyB4HwOfjXDIBOVkYzGKIsw6DqV+gUHMIgyBHKfD2co/oUqpCZjGMsfRYFEl0BAcBEH+GI+ycAGLHAUn2sFHDy8wMQAvxgucMuFwjqYBBDEIgdACIqGGDHFkDdDjPAsZWQLYZhQCGDLFLTmJePOQJZt51SThQQBeKpgABgPMcl17RKADDXDJU0NIACACIAFiDPyc8P0Af4M+ftgwWwcOHCBkwB+FQLoF8AtLMUcltVayFDodlaaGcp5GhlWDDVgBNe3pZchQEmk5wqCsREAQHAmAxEbm2GAGBuHTlpt44CbGUD+HFwBkMTPkMAB/z8OMADYGR1ZBcA6kAhg6EKhSoYon8MgIBW8+GWq9C8q4nkKtSZaVJGFMh+BQQAs1/zhLqiAGBCDad0RlFATeTMFju2gCGQA8u9+D/7+TmJl5MA6ASFZp9Ctm4CCAGBWJbWWfKVXIXy1REFcp8CAoC5b8xi1WIBwFgNhzQmBAV0/fwAfvCR07FwqQAIeGTQQnOxBMx+gyE0P1AV7E8Iiyd+GHg5QERNTePWb8lVGGgo5GRRIHIFBAAjlzixLyAAmNjjm0y9Y/DjBM3mfH6w+AHWYBWDn58bAKna7dmzxzgHS8VxBkBus7oErPoUssVQtRSqqWnc9iw2WwpVwJRchcn0bZO+xkEBAcA4jEIuboMAYC4ePGm6oQD7+e3fvz8rGlf184OPH3z9YO0DxNn5+TnJmZsB0G4JWc1TqEKznwTWuIZVrkKrYBMv4C1TXBQQBewVEACU2RFIAQHAQPLJyTmsgOrnB8jD/4sUKWJAIax9sPoBaHT8/PwCICyCWG4FXMblcAoCsWsjg3SYCazNuQrxf4wHoq3Z8qjjoxgXXaUdokCcFBAAjNNo5MK2CADmwkGTJhugZ07kDAAEvAAuAH6ADUCZrp+fDgCiLoCl6gOYKABoZyk05ynkXU3UyGOGOLcE1hgz6MVjwkmtJVehfKlFAe8KCAB610zOUBQQAJTpkJsUcArwAPTxrhwAP/j6hbXcuHfvXgMs8ZNMAOgGhVa5Cs0+heoYWMGynV+h5CrMTd9MaWtOKCAAmBOqJ9A1BQATaDATuCtOiZzxGfv5ATaKFy9+QiLnoNIIADor6LarCVsLGRixJO90SK7CoDNWzk8GBQQAk2GUI+yjAGCE4krVgRVwS+QMix/gj61FWALGlm1hH24ACMuWG9SE3San+vz4AIbdPoY4c7AJrqPue8wWQzdrreQqDHuEpL7croAAYG4fwRxuvwBgDg+AXN5WASs/P4YETuQMKOAADyzNAgiLFSsWuqoCgOFIijGCHyB8AFUw5OAQqwhkXUshl+O61CVkrtcNMsPppdQiCmSPAgKA2aNzwl5FADBhhzbXdszJzw/QAF8/QAR8/FQ/P8BfVAC4b98+IwcgrmflAygWQL3p5uQDaLeriepTqBMxLLkK9cZCSuV+BQQAc/8Y5mgPBABzVH65uKKAk58f4ABLvQA8BGIgyMOctBifIRIYPoBhHyoAAmJ4X2FcBzAKKJQlYHfVvURMc/SxCoZBElijdZKr0H2MpETuUUAAMPeMVSxbKgAYy2FJqkbp+vkhVQggyy6RMyAMkBglAMIKCAskDrZMwSopFkC9KesFAK1qdNvVRE1No+4EY1cX/s7zDy8QGF+Mq3n5WMfyqKeAlBIFwlNAADA8LZOyJgHApBz22HRaTeSMRvFDGw9l9vPD3zifn1PDowRA+ACytQ8gytYkzomH/zM8AELc4CPqAYhDEIhVH6PYN9kugTXGwMuuJn/++WeWZZnr5K3u0BfJVRj1rJX6vSogAOhVMSl/nAICgDIhckIBJz8/LKnCygaIAfhhyVfHeR/AiO3gUlNTQ+sS7yiCegETvMsILwEzqOLasBLxsqVqIVSjXHX6EUbjkwkA7ax7Zp9ChnUrn0KMCwAQFmar/ZAlV2EYs1LqCFsBAcCwFU2y+gQAk2zAc7i7TuCn+vlxgIfVw9iuC2EDIEAU4Md+Z7DwARCsfACxfFi4cGGjaWpOPDXSNTuhENfFcjiANU5HFBZA3f45jQvmGeYfopPDSEvD1kJeTs5pi7CuRlIudykgAJi7xit2rRUAjN2QJGSD+OHLQKVa9fAZgjcALAxZdn5+TuKEBYAAAVggASsAUVgh0W60Cb+7AaCTRcpslYoKCuMMgNAAusbhUJePMd4MgmibefmYl/ad2i25CuMwqsnTBgHA5BnrSHoqABiJrFKpooDq5wfIwwFrGS+v4m+wkHA+P7/iwWKHaN0SJUr4qkIFUViCAHsMolgeDAKA2Q2FAoDepgDGHpCvWnGdEliry8huy/oqFHKrJFeht/GR0tYKCADKzAikgABgIPnkZAcFrBI5A/bw8IMFkP38AH4ALrcHqZvYfgHQHHBiBaKAA4ZUswUwTH+7sJaPBQDdZsvxn6sAaDUPGeJUCy40Vi2F5ghkL5ZCDjbBv+ZgEx3Lo7feSulEUUAAMFFGMof6IQCYQ8In8GWd/Px4aZWXAWFlCwp+LKUfAFQDTpxANLsA0K+lkOGDfc0EAL19wdwA0G5crCKQ2WfUTwJrXMcqVyH+jpckhkOBQm/jm6ilBQATdWSzqV8CgNkkdBJchnO0Aar4Ichwh//D+gdfPzy8sF+vHz8/JxkBPXv27KG0tDRXtb0GnKgAiP7hhwNUwrQAujb8nwJulkLojjYCauMUgAA/uzj5ALLe7PeJJeCgLyS6Cax1cgtinHFg/gEAec6hjZKrUPfbkrjlBAATd2yzpWcCgNkic0JfhGEEwMHpUVTww0Mflj88vGDBQBkAYNiHDgCirYjYBYy6JZZW2xc3AHSzFGIseIkSZe1Sn4Q9Bm71JQMA2o2N2aeQX5LUpWOrHJJsneQUNQyF/L2TXIVusy5xPxcATNyxzZaeCQBmi8wJexErPz+GP07kjAcVJ3IGfOHvUQIggkCsrDgMohgMXu7VHRgALPoBC5GVBTBuKVd4CRjtZYuUXUoa8/KxriZ+ysUdALMzbQ5bzNXxUaFQHRfMLzfrpDkCmcFQtRSqLwJ+xk/OiZcCAoDxGo9c1xoBwFw3ZLFosJOfH0ADwATQA/gh5QcDGQAQP8WKFQu9H3iQ7t6924gCVgGQ2wNw85JYWm1gbgVAK6BxWz5W05+EvXyMscf145IGhseYl4CzEwCdrLgqrHMCa9VSyMvHbsvVZijENXlMzcEmYY916F9wqfAEBQQAZVIEUkAAMJB8SXcygx8eUGY/Pzyo4OOHH0T5ArbMiZyzEwB12qM7gIkEgE7g4ZSnMAxLoQCg7oz7Xzm25sIHUPUvRImwodBsLRQo9D5e2XmGAGB2qp2A1xIATMBBjaBLbn5+7FeHBwiWV3m/XHNTotyvF23ctWsXFS9e3FimBbS5tUdXKicAjIv1SO1LGFHAUVgK4wqAYeilO5e8lrOaX+rYqNBuhkK25nqxFHL7cA0GTDX6WCKQvY5gdOUFAKPTNilqFgBMimEO1Ek1kbO6hITfVT8/zp/n9LDJDgAE9OHhpdMeXWHggwVIwBKh2QcwUQHQq6VQxxoFAMQBC3GcjpyI5Nbtv+78coNCq2ATpzbY+RRKrkLdkYu+nABg9Bon9BUEABN6eAN1Dg8AAB5Hk6rLQapfHe/b62ZlYGBERG1qamqgtplPVv0Oefs2nfboNkIA0F4pJ0uhGQrxAoBxEQDUnXlkfP/8BhkxxDntauI1gTVaruYq5N8xpuoeyGIp1B9jvyUFAP0qJ+cZCggAykQwK+AU4KHmz7Pz83NSNKz9evkaan5B+EgBMLAEHHaOQQFAb98TJyhkC1IYPoXeWmVfOs4WwLDbxlBo9vdkn16vCaxh1UVduB9wwAqUllyFYc1O+3oEAKPXOKGvIACY0MPrqXMMfgjiwJu8avHDZ37y55kbAADEvrp+9+tVwY+Xk9FOpMhAm3fu3JljAOiWpsPTYAQsHMRqFPDStqfzHGLQ4IAGnKCzfBxVu1AvlvUxv3kv4Civ5bXusAHQ6voqFKpgqAOFVn6dOO8fA0NWblD+mzn6WCchtlfNkqW8AGCyjHRE/RQAjEjYXFQt3/wBZwxoWKLlPGLs54f/cz4/v93zs12b+VpO27dFBYCAYs5fiAcyfucI5zB3kfCrq/k8Xb+xsK6nW4/ZB9DNb42tUbppT3TbYTWn4gqAOQmnOrua4PuAe4NOah87v0LJVehv5goA+tNNzvpHAQHA5J4K5kTO+P/evXuN7dRU0PKbP8/qQcv1e1WeoQaWPzs/P0QBI8m0XRSy12tyeQFAv8odf55OEEhOQCHmOuYVAofidsStbRgf1acQ7cMBCFRzSFrtauJkfWQ45Lqsgk0kLc3xCgoAxu3bmsvaIwCYywYspOba+fnxdmrsT8cBHuZ8fn6bobNdm7lutBUABj+8/PnzGw9pOx+/qAEQ10YQC2BU9ZXS2anBr2Z+zsstFkDdvkUNhXGDLFWXOLcN7cR3k2HNKoG1asUNAwolV+H/ZocAoO4dRMpZKiAAmFwTg9/e8VBh/x51316ADG7obqDlVzUGQLvt2tR6OQoZOfjQRk7r4nTtqAAQurCzO+AYh2oJwf9z2o9N1SWuAMiwEEYUcJhQGGfIYteMOFonMefw3QCU8feC56FTIJCfvanV5WP1Gsmcq1AA0O+TSM7jh9jf3rpyJLQCfDPGgw5wYA7wUAMqAGnwAQzL6mcGE6vt2szi+11+Rt1w5AfAhnFAN3UPYSwvQzsrH0A8AM1Q6OdBF0a7kwEArXTyC4Vxhiy0jbcxDGNuhF0HABAuFzrfOScoNOcp1EkjY+dTmCy5CgUAw57NSVafWAATf8CdEjmbAypwEwdE5RQABk0zEyYAQhss9+IhA13wf6SYMQeB4HOUU6OAdawfDIdR+DUlKwD6hUIGd1jZwswfGcbdBS8gmHPww43jAQs9vh86AOhnfPwksMZ1OD8hB7Kgfbx8jPFGNoLSpUvHUVLtNgkAakslBW2+fGIBTNCpYefnh+5ymhBzQAXO4e3Uws6lh+ty/WbAxN/DSDMDAMRD3Lwc5WWIVQjlYBPohPYVK1bMFgBxXSerqR0UWjnPB7W+CgA6j7jZUsguETjLjyXKy/zyWhZzD+OpE2Xrte4wygMA8X0LM/CKLXtOCaxV67oTtLN1FwDN475q1Srq378/jR8/PgwJcqwOAcAckz4xLiwWwMQYR7UXDH64eTr5+eGmjZuiGfSiSqViB4Dm7eSCwNuePXt8p6pxglDAX1AAdLJ+mJ3nGQrNaVB0Z2ucARCAFWSMdTXwUo4hgZMZh7G/rpfrO5VNRgC0+64wwPH3hXcp4uhjq11NrCyoCxYsoNdff51GjBgR1jDlSD0CgDkie+JcVAAwocbSsBTgYWYFfuzLBqiAtcrujT2qQApWmgET/4f1gP2b8PANuvzmFwBVCLXyIVT3MLZbAnazAOrONKuHHI+nVaCJVb0CgLpq/13ObpnVbIli8MhOS6FVomVvvYu2NNwfYJ2MYsXAreU8Pk67mnAZvOzy/WX69Ok0atQoevvtt90uEevPBQBjPTzxb5wAYPzHSKeFqp8f3+T4XxVuOJGzE2hlBwAC9vBg87OdnJMeAEA8jHSjTNWlcGiDc620UQEQWuP/6jIt/InCAkA764d5OYyh0CrQRABQ51vzvzJe/OyyGwp1cid66224pXMSAO16oiaw5hdilP3ss89o8uTJVK5cOWPXoEGDBmn7AT7++OP0/vvv0/bt2w0LdsOGDenZZ5+lunXr2goKl4tRTYoAACAASURBVJTbb7+dxo0bZ9wvLrvsMnrttdcMX+IwDgHAMFRM4joEAHP34JsTOasBBQAGWNhwA3SCG7MCYQZSqHVzRC0eGIAW3r4tzBFAkmnAnxsAoi2cWxA3czd4y2kAdINChkPVUsiBAzrRlGGOgVNd0DyOS8BBl1mjhMK4AyBefqxcSbJrTrldh1MP4Xu+ePFimjRpEs2aNYvmz59vQGCFChWoQYMGdMUVV9BNN91kW93y5csNWAS8YdXilVdeoQEDBtDmzZttVy4AfLj/YqkZc6RLly7GfW/06NFuzdb6XABQSyYpZKeAAGDunBtOAR6AQtz08OPHwuZ3GdVJSdUKifYhnYrfqEGn67gBIHRTt7bThdA4AqCVDmz54GVq3s7PyUcqO78BiQqAdoDO0cVBfArDzJ0Y9lhzBLzbC1TY1/VSn9Wcw9IvIK5Xr170008/EXwCAXY9evTQqhpQ/sYbb9A999xDW7dupZIlS55w3rp166hKlSr0888/U+3atY3P8Xu9evUInwE8gx4CgEEVTPLzBQBz1wTgB4pdImeOpHXz83PqdZgAqG7fxlZI1B9mrj61L/v27TPA0ipiElAE6yP+5UhhXZ9DQCPORfRyTiwBe52l6hKwCoUMIqjPvG1XdlgKkwkAw4LCuGqG/uUGALRKVP3iiy9Seno69e7d29NXC1HD3bp1I9zD8H25++676fnnn7esY8yYMXTNNdcYqzDqgXvTyJEjqV27dp6ubVVYADCwhMldgQBg7hh/DgzAzQzwZ/ZVYwsVyvGOGbpwY1bAzYqmo5jTEmtUS8xolxUAoi28w4kfiyjq1QHAOC2DOfkAWjnOm6MpdVNs6MwFtYzdrhFe6wm7fE4GWrgtH+NzXjbPDkj3oi0DoJoD08v52VHWKlH1E088QfXr16euXbv6agLuYcOGDTOseJ06dbKs44MPPqB7773XWCJWjzJlytDAgQPp2muv9XVt9SQBwMASJncFAoDxH3/Vzw8PKgAgllBxsJ9fmJG0QQDQvMRqtX1bmBZG8+ipAMg+h3gDD2IRTTQAtJrxZggxWwpVIAwCIXEGQOji5juaXXcLdTzwcocDf8ORE5Zbu37zi0acAdAqT2G/fv2obdu2dPnll/seUowHtrT8/vvv6ayzzjqhHrEA+pZW+8QU7ZJS0FIBAcD4TgwrPz9OSFykSBHDqhVFJK3TMqqTWuZdReB0bWWFjBIA4ZAOWMEyMO/iEdQimgwAqAOFdilQoLfubiYCgN7vN2zBQtomJ59CrztmeG/JiWfENdpcbSnuAwB7Ne3VbbfdRjfffDO1aNHCtwy8M9Dw4cOpY8eOJ9QDP7+qVavSokWLsnwA8TsCTtauXSs+gL6V/9+JAoABRRQADChgBKc7+flx5Couixsa4Cbs/FsMUbpbT1ntnOG0/Ow1VYsXiQGvbL3iXTz8LoWr18XNHnXjjd/KBzBuqTCiejCzK4LZrxBa6ex7LADoZTb/XdZpr1235eOooZDTKOGFNK6H1Xeze/fu9PDDDxswpnsg6hc+fYgE3rZtGz300EOGL9/SpUsNf0KrAxZGuI98+OGHBrxjyRlaIQdhGIcsAYehYhLXIQAYn8HnhytgAw9Y1arCS6ts1YoqihZq6AIg+/kBSr3AaJAlZrvRQltgDcVyDx560CdMMBYAtP+eqFCo5ipUoZDhEHMlyL6xUX1b4xxp63WvXbfl/DChEOMN7bAEHNfDKk0NLHaI4q1evbp2swFzP/74o3F/xJaQjRo1okceeSQLItevX0+1atWiiRMnUtOmTY164SvYp08f+uqrr4z7OepAHkCcH8YhABiGiklchwBgPAZfTeSMFqnwpy6tYlkVy8CwREV1ADJxfVgX7Q7eVYTLeUnpAgBEP8La21RNMQPQYH+/MPURAPSmphMUMoCoFkNvtYdfOu4AGHSv3aigEN8LvHjFFQDtopRbt25N8NGDNS83HwKAuXn0YtB2AcCcHQS27HEUpgp+5qVVABP+xkuRUbXcCQDDCDrx62No7q/VLh6wlrjBqx/dBAD9qHb8ORyNzTuo8DKyed9jDnIIfkX9GuKcasUqiEG/Z/Ylw4BCfC/wMuj0shhGW/3WYRelfO6559K8efOMBNa5+RAAzM2jF4O2CwDmzCBYBXiwr5qaQgWWNdXPTwWRqFqOBw7aoL7VqzDqN5UKtxcAiCVjvzdfVR/zLh6AVxxhWyQAm/BdTEtLs/UBNDuaRzU+OvVG5QOoc22nMmZ/NrYU8tIx/sXfzFDIgSZBr293fpwB0CqIISodvEIh7kewwMcVAK2ilNFHLN/Cd0/dzjEqTaOsVwAwSnWToG4BwOwdZH7g8f6UZj8/zudnt7SqgkhULQcA4sYJZ2X2rWPnfdzo1Wg6P23Q9TE0183WUtXKZ156toJXP200n6Pqzste0IGhPTsf0jr9yS0AaNUXNdKVA3oYCq0CTXT0cCsTdwCE9T9Mn1Y3PdTPnaCQ5z9exNhyG0bQlZf2OZW1+h4wAC5btsx2C7ewrh91PQKAUSuc4PULAGbfAKt+fnyT5H91U6igDjgWwwcwqhstYA/AA4sWA1UYqVRYaT8AqO7iAcsh2mbV/6gBEJZFvgb6ww89jB8eggDSqMbFy0zNzQDoBoXmfY8ZCvlfP/rHNToZWsQtwhxtYiiE/x/mGr4H2ZVM3Mv3wCpKGW1v3LixYQH0M1e8XD/qsgKAUSuc4PULAEY/wGoiZ1xNtfqpfmw6aUuyAwABOLwzAm/fFuaNUifIhEeF/cZ09zVmeA07LQUsswBXPOgwTmyRYgsVLLp8xCFRb1wB0GtEq5t1x5yOBuPiZ9/jOAOgVRRr9HctvSvge4Ex4O+ElfVWfVGKaocZu9ZaRSnj3nbBBRfQ4sWL9ToZ41ICgDEenNzQNAHA6EbJzc+Ptycz+7E5tQh17tq1y9iTNmz/FRW2AHzYHD3sa7BFwy1QA23hSGMvu3iEDYCq7yPaDt1xoG2qNgw2aKvqz4bz+QGoWqp0Eyf7nZ3JAIBW2piBEP9XodAOQJxy7fkdg7DOAwDCAh/FdzFoG9220GNLIb8o8b/ZBYVWQSrbt2839vOdOXNm0O7n+PkCgDk+BLm7AQKA4Y8fvwXj5sOWIjXAww/YqK3cuXOnAWdh+QSZYQu+bWh7WLmqzAq7LdPycjgeFl6XnsMCQNX3EXrAwsHR16yXGQCtUnWwz6f6AGQoNC9dhvmAT1YANM81XQCBBRfjjDGM04H2w2IeZwCEXl620NMdkzB8CjGu5iCVVatWGXv0Tpo0KU5D7astAoC+ZJOTWAEBwPDmAj/srRI54ypqvjosrdptlebWIlgAkeg4aDAGroO2cnJpbhMAFUuugMwoDjsANKe9QXu8Lj2j3dCZ90r2035VEwZQjC37XrIFUG2bl1QdPE/UpMnoe5iRrwKA9iPvFNRgTpIcZN9jP3PPCmATDQCtdIkKCnEv4H3S+bo///wzDRw4kD7//PMwhihH6xAAzFH5c//FBQDDGUM8cAEfWBKBtcjs5wdAwM0oDJ+6MACQAQGwZ/Y95EjkqADQbKUzW9uCbG8XBACdAFT1vbQDwCA7XJihUE2H4ifIIc4A6JbUGGO4Y8cOKlmyZGjJwt2+5YAsvFAB+FRrLc5TodDLvsdu19T5nC2ACD7y+jKkU3/QMlFGT4cBhbiX4bukppyaMWMGffLJJzR06NCg3c/x8wUAc3wIcncDBACDjZ/q5wfAA9ywnxhDoW4Ag25LYInCA8HL7huKxdcAVbQTD2LcGM1LyegHHojcD9126ZZTAVC1ivrtk3pdPwCoA6Cq7yUexHiwmC2AQQDQzipi9ifU8WdDXQyAcQMHN0vp99Om0eT336f8+/dTRpEidEmPHnTueedlyQPd586dS+tXrKD0ihWpyTnnhJLz0apdZkshj0V2QmFcx5EHJEoADMNSiPsLB6lwfdiqbfr06fTqq6/q3rJiW04AMLZDkzsaJgDob5wY/FRLDS8dwnIGS2CYufPUViIhMS/X6rYe7WXYctu+LWoAxEODgyjwbxhWUdYBuuNH13/RarnXytKSEwBoHls3i4h5azXMv9wEgOvWraP37r+fbk5NparFitHKPXtoyL591HvAACpXrpxhyRn8wguUOXcunVWgAC07eJA2VqxIfZ98Unu87b4vbmCqvkBZBZrgcytLbVCrXdwBMA7BM07fC4wLxgAvu/ziPH78eFqxYgU99dRTurfP2JYTAIzt0OSOhgkAehsnXqqzSuQMmMA+t3gQoJzXAAbdlngFQN0cg3z9KHcbgS6IaoR+XqKfdbXRBUCv/oZuAJhTD0InKxU0w7Im748cdeSxzhg5gdaECRPo4Hvv0TXVqmVVNXz1akq7+WZq1aqVkbZjwuOP0/2VKtGWTZto3dKl9PHevbS7Th26rX9/atiwoU4TLMvoAqCdVSoqKIzrUj7rkFPz3m2g+XuBl02GwEcffZSGDBlC1atXN14YunfvbsyZunXruu5K9MADD9C4ceNo7dq1RoL8888/nwYMGEAVKlSwbUrLli1p1qxZRoAM2oPvH8655ZZb3Jqv/bkAoLZUUtDm5pUpyugpgJsxLymYEzlzomLAE8DPLlGx3pWcSwEyUb9b5J1XyIkSAFULJN+Qo/AxBAA6BbDoLPfaPeQ5/Y7VEnCcHoToI+c/AwAynKBfUUYe68xtJ9CaNm0arRk0iHopAPj66tVU8847qWnTpjR16lTa8sYb1D41lX6bNYvOKliQphw8SCvKlqX16el0z8svG36Dfo6wd3LRDfRx2/fYKpGxn/5FdU6YeR2jaKO6RI3vAazMgMDffvvNALL58+cbabUQFfzMM8/YNuGhhx6iq666is466ywjEfytt95Kv/76Ky1cuND2HOQabNGiBT322GNRdI3BNiWyynNBxUnd+TDGRyyA7ioyvHCme9WSokIWLFpY0oxylw60FulI4G+G4A07WOElaACA16CKsLebQ324aarReNBJd5nWfYT+V8IpgMW83Os15Qen38FD2+wDGCcAhBrmpUNdIIl6z10nAMSLzUsPPECNd+ygmsWL0y+7d9OC9HT6zzPPGEvZq1evpvf79aPOf/1FJVavpnJFitAL+/ZRm0aN6Mf9++nUO++k5s2be5kuWWXDBkC776U5Hx5bhqwCTVCHVSJjXx2M6KQgltOImnRctVYJvp9++mk644wz6Prrrzcsc+vXrzde7E899VTtJi1atIgaNGhAfE+wOhEAiPn4+OOPa9frtaBYAL0qJuWPU0AA0H5CqAEeKKWCHz7D2yV+GLJwE48qSbPaSgAgrqlGtvHnnGMQ/wf4eYUchocwtpuDRpzsGtZKtJfhCX+PwgJoBYB+LaHmmZGbAdArkPiJPNa5tboBw5YtW+ibsWNpy/LlVLZmTbr48svplFNOMarGfBr16af09RtvUOX16ynzpJOocoUK1L1WLXpp/Xo65777qFGjRjrNOKFMTm235hT9zSlo8OISN19OFjA7wNnXgP5zkpWF8sEHH6SLLrqI2rdv77tqLOUOHjyYkFPQ7gAALlmyxHgZS09PpyuvvJIefvjhUIKW+JoCgL6HUE7856YqS8CmqcBLaHaJnO2CKVQ/sTCT+ppnqtVeumYrW5AlaDXliR8ndujglOw6yjQzfF1EMPtd7rW7M7gBIKDJD3BHcSfyGzzAc1+1VOlGHuv0IwxgQB63l/r3pwuOHqXmZcrQT7t30+IKFejeZ5/1nTYmpwDQDsw54phzivILaFRgrjN2VmXipJtV+6xeOO6880667rrr6MILL/TV7W+++YY6dOhAX3zxBbVu3dq2jtmzZ1PNmjWNbArwX4XPYY0aNejjjz/2dV2rkwQAQ5MyOSsSC+D/xp0dh1U/PxWA1GAKWLOsICvsXTrsbrocyRtFqpkgIKuzi4cKaWF/6ziCGY7anODaryXU3LZkAEBzn3Ujj3Xz44UBgGgjfLkmjx5NW1evpopnnkmXtG9PpUqV8j2d4rrfLm9lhvuNVUog3AdyEgrjqhtPBKv5duONN9J9991HjRs39jxfvvrqKwMehw0bRldccYWn85F6BsFMWMFx89/WrVgAUFcpKWepgADg37IApHCztfPzw5ukVdJks6hhJGl2m6poC9oLP0D87mWvXLe68bkfAPSyzBplmhn4PuKmjyPM9DKoj8cWelv5ACaCBVB3flhZCnGuW9LksABQp51eysQVZKz2suV+WUUeh2mt1dEvzvsUo/1WFsrOnTvTyy+/bFjjvBwffvgh9enThz777DMD5LweDIDwdbXz3/ZapwCgV8Wk/HEKJDsA6vj5OSVNNk+nIEmadacmp1FR8/n5Wap1up6uJdPPMisAEH1AsExYB7cDQIwjzL2SuY1OAJjdCXHddPO7BOxWr93n7MumLh3jdxyqhQo64eEXxjaGfttqdV5cQcZqL1unMbDyKWQwN29zF/SegWvFeZs69NtqXNu2bWtAXNmyZbWn0GuvvUaPPPIIjR071ohIdzu2bt1qRAgjCAQrEL/88gvdcMMNVKVKFePaYR0CgGEpmaT1JCsAuvn5sZ+aW9Jk87TxmqPPy7TjlBBoGx6qiKINehO3uz4AEPU7PajVXTy8LLOGnWdQje7F0gogI0y4FAD0Mkv/V9YuwIGhUAXDqOaxbsvjDIDmvWx1+4Rybkv4QaCQATCuASp2gAoo++GHH4x8froHdMKKCy/dcvQ2clcCCBFJXKtWLcIuI/g/XBRgafz999+NVaUyZcpQp06dJAhEV3DNcpIGRlMoh7fGpAoC4YcSO1ebk+MyTMB6wVDj5eGkm6PPy7Cp0bQIMsDNCDeVokWLeqnGU1mnpWwVRP0ss4YFgGzlUncTQdvgYyMAeMxwD4jbwxmgxYEybC3khykDIcZw5syZtHbxYkotW5aaX3hhViSwp0nsoXCiAqCVBAyFZp9CthSawdzu/pfdVmYPw2kUtQJU/A2R4sjhZ94C02v9cSgvFsA4jEIubkMyWQDVRM4YMhX+gkINT4EwAZCjabEEjbbiYQ6LnJ/9br1OUaulbLRH3UcYgOwn2jlonkGnZeegdTvpxJpgDGD9VI9kXwJ2m1+wKH8zaRKtW7aMTqtXjy5u0yYrjZHqT4iXg7dfeYVSZs+mxoUL04ZDh2hRWhrd/sQTVLp06Ugs3nFeysTLDea0VconN829fO4HCuO+S4kVoDIALlu2LJK55EXzMMoKAIahYhLXkQwAyMCAGwJDH7/Vmq1rfqGGp5Bbkmbdqea0fZvudme617IqpwIgNNLdR1jnmgxpfhJmuy07Rw2AmB+cnFddPkO78P+wovt0dHQqgzHbsWMHTfn2W1oyezaVrlSJOlx9NZUvXz5o1a7nA/Y+fv99+nXmTCpSujSVrV6dfpgwgc45eJDqFypEPx45QgcbNKCHn3vuBCvMypUr6aMHHqD+FStS/jx5DCvOx6tXU2anTnTpFVe4Bpm4Ns6iQNwBEPetsIIGvOij+nWqvp2og6O+eeejOGwzaO6bFaCiT4j+Xbp0qQCgl8kQ07KyBBxwYBIZAPFl58he9s1TfTicctX5ldUqR5+XutRlTdz08eZvXoKJMo8et5X1wo1e3cUjSH5BrttPnkGr5V6rpakgcOk2TlgW52vCH0j1ccPvOGAdVMHQi/uA2/W9fA69+t56K6XMm0dt8uen3zMy6PvUVHpx+HAqV66cZVVh+HRB/7t69KCKv/xCp+bNS0PXrqUyR45QIcyjIkXogbp1qUbRonTfpk101XPPnbB/74IFC2jugAH078qVs9o4dcMGWt6sGV3Xu/cJqVBQyC3y2E23MPrtdg2/n+O7nlMAaNVmFQrVrAkMhea5n1PzH+2x2kUFL2rNmjUzloAT4RALYCKMYg72IZEBkJdPcBPi3EsAGLMVCQ/zsG5UiIrjwBEvw6our6I9Ttu3RZlGhdsMCyDgD9dSd/Hw0ie7sl4A0Gm516p+L3Xr9kWFT/ivYWx4CZjnDZbp+SHIvlVoO+/1yn5VvLuD7rX9lkOy5GduuIFGpKdTwTx5jGqe2byZCvXoQbf06XNCtfPmzaN3X3iB/li7lkqVL0833HMPnXfeeZ4vj71V37jtNhpWujTdtXQpXXHwIFVDap6UFFp50kk0q1gxeq5hQxq4cSNV79uX2rRpc9w1MO+ev+MOur1IEaparBgdOHKEXl23jhrdffcJ0ZcqjLDmVpHHbvvtqgCInUjg0I8o8urVq+e4nxis/WhfTlgA3QYfAIj24ftglZIG3w2r+e9Wb1ifc/vgOsMHrNNXX301zZkzJ6zL5Gg9AoA5Kn/uv3giAyAe0ngwMADiIYz/48aAGyp+wgI/ngmwluGGrd50nGaJn+XVKAGQ/Q4Bsrh5I1Iu7JQduIbOlnluy71RA6AZPjF38LADoOPlAgfPHzwIcagWZtVCyICCMmwlYSiMYvkMuxVMvP9+GpSeniXTp9u20aKLLqKHTZveA3ru6tKFbs/MpPOKF6f5+/bRwKNH6bkPPqDKiiVO526HXGdj+/WjASVL0nULF9LwAgVo54EDtO7YMQPobs/IoJebNKFHdu+mfkOGWNY/a+ZM+mrwYCpz+DDtyMykKi1b0r9uvvmEeQjoMPugqpqbg0zs9ttlwF8wbx4tHjWKqh47RlszMynvmWdS5x49chS+zPNKZwyyq4xTjkI/4xB2u63ah+hc7ATy7bffhn25HKlPADBHZE+ciyYyADLs4QaP4Az8G7Y1yzwTYAnCdXVSDOjsLGI108KKojXXre7iASiBtSsK53M3ANRd7o0SANXUMhwNzsviOgBo1TY7ixVbSsJMi4I8ZD0vu4xeKlyYzixcmP48epT+vXUrtXnsMbr88suPax62tFoxYAA9quRFG7BpE6XdeSd169bN080OGvVq357uzsigjzZsoGsPH6bUzEzKV6IE/bBrF31asCBVOO00uuTmm6lj5862deP7unbtWkpLSzvBbxEvJ5PHjqWVc+ZQgZNPpnqtW1Oz88+3fZmD7qqFEL/jb7yLBv79448/aNQLL1DP9HQqcdJJxr3ik5UrqWL37tTknHM8aRBm4TgDoJcchdAku6HQqn3Ix/fss8/S6NGjwxymHKtLADDHpE+MCycyAPJDnJfn8ODWAbMgI6sDgHi4oBxu7n6ANOxAB7U97HcIX0boFcXSEwOgOVmz1+VeO8jSsS7ajbGVFmzlCwqATlCopuRQ4cSvPyHqGPnpp/TBiy8aFq1Nx47Rma1a0YNPPGGMq3p8+eWX9Muzz9ITZcpk/fnpzZupzL//TV27dvX8dcDy2qDHHqPt69fTnq1b6fISJahEqVL0XZ481LJbNwNA7fwQdS728dtvU9F58+j8cuWMJeJxW7fSmTfdRE2aNNE53SijQiHuEwgKWDZkCHWrWtUAwzwpKbRg82Za1aABdfjXv7TrDbsgosvRnrgEF6n9A2AFyVFoHgfVdYLhXF1C9rpaY9U+7M/7/vvv0/Dhw8MeqhypTwAwR2RPnIsmMgACYjhTPfts6S7N+h1hpxQtYQAO2hVWoINTe4IGs7jpZ95pxM9yb5gAyEvfvLUe5ok5T5gKgGgvgxraEaalRoUTv/6E7NeG+Qi4QSJa+LRZPUQRLXxHly7U7cABapqaSvP27qV38+en//vwQ9+ghiVyLLfh+7dkyRJjqRbO94j8hlXZbw42+Ai++9BDdHeFClQwXz5jCizbto2mnXIK3XjvvW7TzvJzQP+aNWto/MCBdHP58nRS3rzG2H6xYgUV7tSJmrVocdxuJlEs2ds1PG7phcIGQLvvsDlHoZ+XIqsAGrhGTJ48mV5//XVfcyVuJwkAxm1Ecll7EhkAcRPBgxo3bB3LXBhDZ5eiJSzAQRvDCHRwa0/UAMiJpgEGvM+yn6TS5jGzsy46jS3mCUCFffyw9G0FSpzjEZ9bAWBUzvqo16s/oRrYoGM5wdLYsFdeoZW//kqVTzuNrrvjDqpfv34YX4nj6giacNkKAH/bvp2mlipFPQIAIObg9MmTafM331DNlBTacuwY7axcmbrceqsBrFZpULIj4jXOAJidEcocZKKOA+Y4WwitLOVWATRY+sULyXPPPRf63M6JCgUAc0L1BLpmIgMgJ37GcGVH8mRcx5yiBVARdhoVNx86N9gBDKu7Z1gBgt9oZt2vBiyAWNZCOxBk4hT1rFsnl/OyjzG0wNzQWYrPKQC0s5K4RV4CUoPmtfSqvVv5oACI+j8aMoRS58+nluXL0/7Dh+mrP/6gWj16+PbV4yTwsPouX76c1q9dS8VSU6l27don+MDa+XGiXaoPp1vksZtO/DnmJ+rl3VN0z8uOctkJgFYvelY+hSjH2nOQkJq6Csu/ePl8+OGHs0OiyK8hABi5xIl9gWQBwOxInoyZwhG68G/zAhdeZpkfKxfO8bKLh9doZi/th0ZIywPwhE+m2SfNS11WZXUAkHNAog28w4rbdeMEgE5QCKDhH5RT/anUyGO3/kbxeRgAiDomffklrZo3jwqcdBLVbd2amrds6WtXGvSRARBzUV3W1+1/lMENuIfgBSns74hu35zKhen2EEZ7zJZy+CfibziGDh1Kv/32m/FChD3On3zySe3sBg888ACNGzfOCErCHDn//PNpwIABVKFCBdtmw1J9++23G+cBSC+77DJ67bXXjPRCYR4CgGGqmYR1JTIAsl8XhhU3KwBQ2F9A85RR4QYPW9xwwk6jgmvqQA7KQQM/u3hEAYBqdC+DVxSWDbd9jP1aZJ0AMCetIXYwyP6vZkuhunSW3fkJwwBA7i/ALYy8iqgHS+ALp0yhPZs2Ufppp1GLdu2orBIV7fXRYOfH6RXGMVcBfwKAXkfg71Uf6A3tvvvuO8P3D3tMwycWB1wczj77bOrcuTM1b97c9gIPPfQQXXXVVXTWWWcZqzm33nqrkUh64cKFTWEa3QAAIABJREFUtucA+HDfHTFihHEP7tKli/GiGXb0sQCg93khZygKJAsAZsfuGWz9wwOXLVs6/ld+JqQT5KgPSL+wE6bPpFWwCSyAnF7FT/+dzrHSxqsF1Kp+dZs/zCfVWhRXAMRDR52DfvwJwxyfMAEwrHZt3ryZPhswgNoUKkQVU1Np6datNLdoUbqhX79Q0yC5+bFZwTi+v3hJiuIlMqh+cY5QRt+srKf9+/c3EpzXrVuXfvzxR+OnadOmBuDpHosWLaIGDRpkvYSbz0PwU5UqVQjJ2OFGgAO/16tXzwiMcrIc6raBywkAelVMyh+nQLIAYJTJk1XLFvxNYG30s8+tl6mp7tVrPg8P+aDLz2EBoBpsAhhhSwZH1GaHBZDHnndo8WtNSQQAtLMWOvkThpmfMGoARD+wVIexwr7HJUuWdP1aTfnmGzo6ahRdevrpWWU/WbWKTu/Z07D6RHWYYZwDHHA9DmrAMiZbAKN6mfTbvzgHqKBPVtbTu+++27DGtW7d2m+3jeXfwYMH06pVqyzrGDNmDF1zzTXG9dUDKbVGjhxJ7dq1831t84kCgKFJmZwVJTIAYkTZTyWK5MlWViVcE3CWEwDIS96cyiTI8nPQoBm3ZM4AQNwQo8hvxnDM+xjDOgctgu5jnKgA6ASFYeYn5MjkqAJTMM5ffvghHVyyhNJSUmhjnjxUv1MnauSSH/DbyZMpc9QoalujRpYUI1atouo33UR16tTJ1gcDQyHrzumr0AhzkEl2pqOxEiHOASoMgGbr6c0330x33XUXneMzuTfSyHTo0IGQPN0OIj/44AO69957CZZl9UAqpoEDB9K1114b2pwSAAxNyuSsKNEBkJfqwsqdh1ni5FcXJELXyww0W9DUXTyQtsIulYnuNQCA0A4O014OdbkXlgu7nG/sTxcFAGIJGP1H+3lfZfOWYV76xGXdABBzLIqdU/y01WsaGJ1r2Pm16e53HDUAYjlv/QcfUKdq1Shvnjy048AB+mjnTur6wAOOvr8bNmygUc8/T+2KFqUKxYsbeQV/KFSIru/XT3tLRx39/JSBHye/uKgpUPB7Tu+1G+cAFWjN2qnL50hsjhQwZ555pufh+Oqrr+i6666jYcOG0RVXXGF7vlgAPUvr+4QU32fKiYYCyQKAYeTOg17q9m3sw2b2sQKAmHe5CHu6MUABcHhXEd7FI4ylIj9R03bLvVZ9jwoAMT6oO4oIYzU3opUPYKIDoHkcvfoT8kM5KgvguE8/pSo//UT1ypXLauqna9bQmb160enK8q65H5gzixcvpoWTJtGfW7dSWtWq1LJ9+1B9tfx+/wEx+F6bE2dHGXms29Y4B6jwXDNrh+VX7AJSqVIl3W4a5T788EPq06cPffbZZ9SqVSvHc+HnV7VqVYKvIPsAst8g3BPEB9CT9I6FBQADapksABjUMue0RZh5CHQCNAIOG8ECiIcCoCvsPHpom5egGbflXqu+qta0oFrgfHV82NcvbOuiCoDQna0wrFeyAaDVuNnlyWNrFTTCuGDOhvGiorZhxvTpdAC+fKeeavz54JEj9N7GjXTFffdR6dKlbacZAJDzYmJco/BL9TvHMed0d04JK/JYt61xDlBBH6z8TVu2bElTpkzxlA0C6VseeeQRGjt2rBEwonNgu0PMJYAjxgWWRwQGjho1Sud07TKyBKwtlRS0uWH/nSgpQQ9+UKN7uqlTVCnYzw9LorqgFTUAqqlmEFgRxQNLBwB5uZcfBF4sO2EBoOr3yOPDD82wdXECwDD2RQ3zKxjFErDf9qnWKswrgB9HUFv5tfm9DubUyMGDqczmzXRK3rz0e0YGlTj/fGrbvr1jlRg7Tprt99pRnRc0aMZP5LFuX6yWWHXPjbqclbsB/ta4cWMj5Y+XrQjh4oCVFn6h5Lk7YcIEAwjXr19PtWrVookTJ2YBIvyQYTHEsjHmO4AQIOnVpcZNJwFAN4Xkc0cFEt0CqAKgVzBTkwUDbnSjR50idINMR05Yi3bhBsb+bUHqtDvXLWray3Kv1TXC2GpO9Xvk8cHNNqoAEwHAYDNNhVLUpAaY4Hccuv6Edi3hvYf37dxJ5atUoRo1argmiE5kADTrpBN5zBHIbkEmdsvTwWZJOGdbvQDhb40aNTLyAIbhExxOS4PVIgAYTL+kPzuZAFAXzIJu3xZ2ihOraGP4/fFSZxST2A4A/Sz3hg2AapobK7/HqPwLeXs8WALwOw6AOH4Ao7IE7DwTnayS5uhXhkPWOMp9d+NmvWUVow6aUa9jtXzspr2X5eko7lFOdfJ9Ss2DyRZAAGDY7gfZ3T++ngBgTimfINdNdADkBzOGyw3MVD8ynb1h7aZAWADiFG0c9V695rQ5VhAa5C3aT/tVPXBtuzQ3YelvHl888DBHODeb6u/GZWGVZSjMyYdMnJaAzUBjTk5t9z1y8ycMKz9h3AFQV68wH0lu2uP7B910/RPDbJtOXQyA8LvjA39D+hfs4pGT302d9uuWEQDUVUrKWSqQTABoBwbsy8Z5rXDD9eIjYhY2DP82NytkFFu1qf3gtDlpaWlZW8lBJzWZc5CvlFcAVPWwir5W2xKG/ua+4WEHAMRRtGhRw4eNg0DYDxFgiHmDtkbh4+ZF70QAQKv+qv6EnBYFf8OB6MstGzZQ0dRUOqNWLcPpXueAS0WcrLd+gVmnr0HKqNqb95pWLbT4DuQ0YKF98NvG/YoP3P+RvmX+/PlBZIjVuQKAsRqO3NeYRAdA3AjwYMZhBQaqL5vqRxZkJIP4t6nLm05WyLB26rDrJwMg59PDmz6WW8O6sesCrGp51LXKhgmA6pI3W/YAFuYoYLMfmXlJTfVxM1uuwtJUHctEBUA7KJw6aRJtmzqVTkX+v4wM2ly6NF16/fWG071qkbXSOq4AaLWMGeS+FOa56vK01V7TXvc8DrNtqAv3fKSyUgFw48aN1Lt3b5o2bVrYl8ux+gQAc0z6xLhwMgGgCmZsUeJljDDhxg8AshVJdxePKAFQhVAsaeImGmS51+qbogOAahAO2qC7H2oYAKiOB0cX44HCVlA3ADT3WdfHLSzrSTIBIKL7x730EnUtW5ZOKlDAGKMp2KardWtqct55WcEmGBOrqGMeS9wD4nRYLWPGpX1OcGq1rSDGJGiAj5e+c2ofvNTz8dtvv9Fjjz1mpHNJlEMAMFFGMof6kUwAyI77eDvF8oCuRcnr0Hhd3vSzi0fQrdrs+qRaRHEjj2pLOyeAxXWhIXSB5dHrFm5+AFzVAy8HuD7awcvN+FyFVjMAWj1w3OaN3XJmGNaTOAJgVBatNWvW0OJ336WOlStnSb50yxZafvrp1LZzZ+NvTj5t+Byaw9odFoC7jb3O57kVAO1eftSdTKK2iFtFdmOnmCFDhtBHH32kI3+uKCMAmCuGKb6NTHQAxE0HNwM8AAAGauJkXYuS19HTsW6hTi/Jpc1t8LNTh1M/zNG9gC5ETaempoZu/UM7rAAwrEATvwCoWj6toovVcQXw4Ycto34A0Go8wlo6TiYABKx/9sIL1CE1lUoVKWJ8r8atWkWndOpEDc8+23baMxTC0oxzGBTDAHCv9wyr8pz2SdeXMYxr6tYRtG26FnF8v9zS0Vi12WpZf+rUqUZevrfeeku3m7EvJwAY+yGKdwOTAQABG2zRwQ0FfkFR+F3xSLstz6pBJ7rJpc2zSCdRs87Ms4Mu/D3KLe3MFsygeQXVvvoBQJ3l5uwAQDvriZozj2FFXc40W66SCQCh2aKFC+mnL7+k8hkZtCszk/KfcYZh/dPZDYaX9gH9TgDOgQ6sdZT3EPTJKpBB5zudHWWiaJuTldZrkAlDvbqsj6XfBQsW0AsvvJAdEmXLNQQAs0XmxL1IogMgQAMgg6VEHLDUIIozysNpedYcdOJ3t4owAJBz/XE+QXOiaz87p+jqyhrBtw9gxVtxheGL6WUJni2f7AvqtNysgr2TBRB9w+fvvPUWfTF8uCFJx+uuo9vuuEM7mbiTjjpLx3hgchRk1KCiO+ZRL2nie75582Zj2R57ver6raoA6BfA/VqqnLSLArJ0x8qtXHa1zWmuO0Gh1Zhi6XfLli306KOPunUv13wuAJhrhiqeDU10AMRDBzcD3CzCXja1G1Gr66i7eIQRUeu2U4fTbNNN5ux15xQvMxxwgh/c4P1aQe2upwOAqhWWd1RxAwY3ANyxYwcNeOIJmjh2LO3/6y+qevQoPXnSSYa1+Ukiat6rFz3Qv78XmbTLhrV0rH1BHwWjBkAfTTJOwfcVh2otBMD/vGgRbVq2jAoVL061zz6bypQpY5QL01Ll1GarSFa/fQz7vLBcHvy0yzzXMa/wN3XpHvdb/F+1AGLpF3/7z3/+4+eysTxHADCWw5J7GpXoAMjRnBiRMKxmOiOrAmBYfm3m65oTNeu0y2tbogJAtJ2TKsO/ya8V1AkA8ZmaAkIti+sDEjmiV3eLPzcA7HfXXbRrzBh6Mn9+unj7dno/M5PqFypERYoWpQVHjlDnfPlo4fLlgXJM6owzyjBsQVv1gYnPnJaOdev3Uy43AeCkMWMoY84cql28OO05eJAW5c1LrW66KQsCzf3Xscqy7roW2ZyELLfxjVvbzJHHapAJ9uzFJgCbNm2iqlWr0i233OLWvazPR4wYQYMGDaJFixZl+ZA7vSi2bNmSZs2aZbxMMJQOGDDA0zW1G/d38FKKl/KJVjapOx/GYCYTAAaxmnnRmv3JsBSF5U27JVYvdQYFQLflXqu26G6dp9sPNegF0IWbNIJMwj7sgnBwQ8ZnAHSrIA+3djgBIKyZZ9esSRPz5aNqefNS1S1baEpmJpVMSaGSpUrRyqNHqUVGBv2yenUoy8BubWUfQMxBfmBFASlu7VA/zy0ACNeH8a+8Qt3Kl6cC+fIZXZi/fj1ta9iQLrrsMu0uM3ir/ps42Wrp0uo5HjfIUjse5/2T0U5OpwXoHjp0KH3++ef0008/GUaAs88+29gTGD8XXHABlS9f3nZMJ0+eTJgPqK9nz55GEKETAKK+Fi1aGOlmsuMQAMwOlRP4GokOgBg6XuLxYzXzM/S4Hi9D+kljonNNdacOp/Lqci9gwEtKFQCgmgZFp11WZdgKyzdlWOZ4Sbx48eJ+q7U9zwyAuD4DMB4Ifnd6UX07zT6AxoOlZk36MiWFaufPTzft3ElHDh2i/2ZmUomSJek/hw9T5iWX0KB33gm9v3aaYw6qAGhXTgWUKNNzxBUAMa4AMF4Chh/hjMGDqWuVKlmSLd+6lX6pVIku69rV9/h5jXzFHMO8VXPZ+b54yCfGdfs87iZe1uBaolr3+/btS/Xr1zf0nDdvnvHTq1cv6tGjh6s6SB594YUXagFg8+bN6fHHH3etM4wCAoBhqJjEdSQDAOLhzG/jWArA9mZRHLgGbjx4oOCIKoUK6sbDFIBml6fP63KvlR5ueyfraMg59fCvuoVblMvxAEDog+VlL1vIufXHCQBxnf79+tHqzz6jx/Lloz3HjtFNe/bQnn/A4tymTWng4MFUsmRJt8uE8rmVBVCnYl1I8ZMvL84ACKsOuyIAvEa89hqdd/AgnZ6eTocyMmjCmjVU/qqrqF79+joyapdx8icElOJzgKkfvbUb4aNgXHdP4a7gHgD4UwEQS7+33norNWvWzHOPvQDgkiVLjPtPeno6XXnllfTwww/buqN4bojpBAHAoAom+fnJBIBu0OR3KpgtXFhehI9bVKCJduKa8NGzAkA/y71WfbfbO1lHJxWGrRJuR7kcDwjHQxwWAPyOB7ubJUynT24ACCAf9NJL9OWIEUZ1V159Nd3Qq5ex3Fy6dGmdS4RWxi8AWjUgrKXj3AKA0GDDhg00/fPPKf+OHXQwTx4q26gRtWzTRns3miADyXrHNT8h+maVZiVIn8M+FwCI772a67Vbt2705JNPUp06dTxfThcAZ8+eTTVr1jRe/hcvXkzdu3enGjVq0Mcff+z5mjonCADqqCRlbBVIJgBkaArTMme1iweu42SdC2M6WvVFtXaFsfTsFwB1cupFCYCc8BtWHd7fOQzN3QCQ065gbHDkpH92mABoB4Vel46DJg8OYwyt6sC4qRZALoPv9rZt24wUUlH4qrr1R4WsoP6Ebtfy+rlT6hyvdUVRHu4PePGC5ZSP9u3b09tvv20Egng9dAHQXO/06dOpVatWxj70OjkpvbZLANCrYlL+OAWSAQB52y6GJvidqTcGP1PCaRePqCyN5nZynj7O+RamtQvX8rqnrhcAjcIfUx0TaIJxDhPC1Ohu9FV1CI8b3EQNgOa5qLN0jLEA1MRtZwt8b3A/CDsa3c99RT3HycqmozdHHbulN/LTTqvUOX7qieocvAQC3NX7/EUXXUSICPbjhhEUAPEyHcVe0wKAUc2gJKk3mQAQQwpowk4gfreBw40XNz92MoaFyQyTUVgaraYjJ7hmJ/YwrV1eAFD1N9TdXzlMAFSX4DGuGA8AWdgJv5MNAJcuXUpz5y6lo0czqVGj06l27dqegNpp6XjdunW0YAHqJmrQ4O+6c+qIKwB6tbI5+RN63UnDbSzMgTNu5bP7cwCg2e2jSZMmRiSwF9DnrUQBgG3btjVeivllwfxyuXXrVlq4cCEhCATX/uWXX+iGG26gKlWq0GeffRaJBAKAkciaPJUmGwAGyW3nZRePKHfRwOzkKGD87jW6V3d262yp5tffUDeK2a2tapAJonvh9I0HJ9olAOgeBWyn74IFC2n48EV08slNKCUlD+3aNZ0qVvyLjhw5iUqVKkKtWp1Np512mtvwHPc5W0lXr15Nb701nYjOppSUvHTo0Hy66qrTqEWLplkpUvzs/+qpMUphq4hRv3WFeV4YVraw/DfN/bJbNg+z/37rsrJ+429I+4KXGi8W0WHDhtGNN96Y9eKDejA3sa8wlpJr1apFEydOpKZNmxJeajp37ky///67cX9G4vBOnTpJEIjfgdQ4T/IAaojkVCQZABDWJk5v4Se3nZ9dPIKApst4GVHGeGjhAPRE4VuCup121AiSXkYFWL+BMk5Wx6h2fOF6sYQJJ3PMC1gD2AKMMYnL8mbQJeABA96jgwcvprS0KkZE45Qpr9OOHRnUtu01dODADjp0aBrdcUcbY8s13YO3DxsyZCT98UdTKl26lhHMtG/fJvrrr4+pf/+bjIerzl7HutfUKZfIAGjVf6ekyWqScCcIj6vVFP3luY97I1vpGACXLVvmyYqtM39ysoxYAHNS/QS4drIBoJfUJipkeI0k9QOabtNJDa7gJNPwc/GypOF2DfVzKwBUl8B1t1Czewj5DZRRrY64yZuX86MEwOXLl9OLzz5LC2fPpirVq1Of++6jevXqZXURmvByW3ZascwaBwXAhx9+g4oV+xedfHIa7d69jqZMGU/58p1Jl156HuXLl5fWrp1FTZtuo/bt22pPKQbAZ54ZRgULdqUiRdKNc48ePULr179ITz99s/FCY17KxHlseVEBhfff1W6ATUGrlCFB6wzj/OxaZvXjTxhXaIbu/HKqAiD+hiVgWADD9AsOY5yD1CEAGEQ9ORc31r9DFhP4UC2AOpGtkISXe/3u4uEFNN2ktwuuCPMaVm0wJ1T2u4VaWACoa3WMAgAxBogIvaRFC2q+bRt1S0mhmUeP0isFC9JXU6ZQtWrVDMss73CCtmLuhO175TZX+POgADhy5DiaMeMkqlLlItq1aw1NmPAZ1ax5GTVq9HcKjQ0b5lP9+uuoS5d2uk0yLKbQaPz4KTRtWiGqWrWtoc+6dTPp1FOXUp8+19nWxVGw0JWjj/E33V01nBoZZwC0ik7WFjxAQTd/QowDXrzw8hk3oLIKyMLL7CWXXGL4ACbSIQCYSKOZA31JBgDEDQHwgsMtstVLJKvTcIUBZ24WSB2YDTKlGABhZcQbP8AKvyOaLehN30ugjDnwBm/2Tn48YSaZ5mtDC/j6vHHXXTT7Hz8gaHtrRgYVuflm6v/YY8ayMC8Bq75X27dvpzlz5hi5AGEt5GVjTu4bVEurMQ4KgPiefPLJOFq2bB8dO5ZJa9YspkqV/kXVqp1jLAFv3TqWevduYuQ80z0YAPHv0KGjaPnyw9iZmMqWzaCbbrrSSJyre+harXQSKFvljNNtR5Tl4uZnp85pfMc4UTX+jcIy61dbnmfqXuB//PGH4cv3/fff+602lucJAMZyWHJPo5INAO0CG3Bz4108dCNZnUY5KJyZl3vVjPZ8XTeYDToL8WDkFDp427eKePZ7DV0AVPMs6m5LFxYAmi2eH3zwAY3p14/GKZ1+9NAh2nzNNfTCyy8bAKguO6HYF198QXffdhtVyZuXNmVkUJ2GDWnIBx8YEB21r5tVJKTX8YLVk33GPv/8W1q1ag8VKZKXLr64HjVrdo6n6tQHM8Z/48aNxotZhQoVfEflqw3wu3QcVwCMs58dLGq4T+JFTLXKsq91GJZZT5NLKYw5hZdVFQBXrFhBDz74oJEGJpEOAcBEGs0c6EuyAaDZrw0PDYYtvMUCMvymiFGHzy+cebFA6kTp+p1SuImift5SDQAapqWKAdAuJ6MK5IAlWB51r8/j6Td5r921EeV3Xv369PaxY3R5vny05Ngx6pCSQs+99x61bt36BACEj2P9M86g948cobZ589K+zExqlycPXdi3L93Tt2/kvm5hAKB5/mBeYzysXkjc5pqVZcbtHD+fY+4iEhP9x9I85oHT0jHmC5Yy/fTJT/t0z4mzn51VomX0S9cyC0D0Eo2rqxnKYfwxpuoeykjPMmjQIBrxzy49XuqLc1kBwDiPTi5oWzIAIOdywnCofm1sXcKDSd2nNoxh8wpnbsu9Vm3yeg2dfqnwwyCMvIlRHHapchjgeCcPr0AeZJcRp2ujXuTzeuy+++gwooDz5KFb7ryT7unXz3jwmS2A3377LfW//nr6+R/3A2g4LCODhterR6O//dZSUvZ1U3fZQEG2qPBSm06ASRQAGGQeWFlmgtSnnou6oRHGYNCgD2nFiryUklKC8uVbTT16tKSzzz7bKK4LKDpLx2G13a6eOAOgVaJlu364+ROaI4+D6orvKeYDXhr5wI4csMa/8847QauP1fkCgLEajtzXmGQDQN4nFg8LLBN4tS7pjrBTChVzHTrLvVbX9XINt3Zz4AvqZEsoIAQaRQWA5lQ5AHVcn2/eWGLStfqp/fMDgBxggnPtttHjejFnsKRUqlSprP2e2edOXQJGxOGl559PvxNRiZS/M1bdd/Qobb7iCrqvf3967623aMPKldTkoovo+uuvP+6Bxf1RgYWh0BxgokKhqkMyACCA78cffqCtv/5KeQoUoC1HM+nHhWWpevUuxtzZu3c9/fXXcHr66duOswiZdeJgBmgcddSx23eRP49rcAraF3Ruqf6EbJ3F38LwJ8T9FGOoAuD48eNp1qxZ9NJLL+nKnyvKCQDmimGKbyOTCQBxg+F9YjmFSdAt4exGFiCDQ/VDMZf1stxrdR1zlK7fWaa2Q7WERhFNq7aRARBjgGuxL5Y5g7/XfnnZZUR1AXBLa6OCJR5aeNDwMpYVAKLd1199NW2cPp16HjlCv+XJQ+/ny0eD3n2XHrjjDmr7119U59gx+iRvXirapAkNHzlSC3jVh6c5Ila1FELPoFp61d6pfBQWwG/HjqXiS5fSWWXL0qEjR+il0V/TqhI3UI2aV2Y1ZfXqV+nBB9sYOzKoB79w4G9m/1a2xEYRdayraVx9E4MGGNn1P4z8hKjbags9LP2uXbuWnnjiCV35c0U5AcBcMUzxbWQyACDfsHBDZeuJX/8w3ZF0gjM/y71RAKDaDqvAl7CCKew0463seMsr3slDV2O7cioAoo/r168nRGVXrlz5OGsm4AmgjjmhE2Ci1qsLgIjkfPfdd2nauHGUXqEC9brjDhr58cd0cOhQerVAAaML+zMzqUFGBr05apSxW4HXg62EDCsMhahHBcKcXta08s3y2le1PF7mJg4eTJ3KlaP8+fIZH301ZTq9szKVGlw4wPj/X3/tom3bXqfnnut93NjDOvvRR5No06b9lJaWn/71r4upbt26ts3JiaXjuAOgOeApyFhanaurudmf0GoLvbfffttYWbj33nvDbmaO1icAmKPy5/6LJwMA4ouPVBxYusPNAg9lBB9EeWCpGQ9i884Qfpd7rdpqdw2dfqntsEqmzG/SuEYUWgFWECSBI6zUMtxvBjVo//bbH9EPP2ykPHlKUoECW6hXr7bUuHGjrN1UAL6AP52lZj8AaDUWvbp1o+ZTptAthQplfdw2M5Oue/lluvLK/1mudMbRrgy/9MCqydYsdYmNLd9IbL1q1UZKTS1CdeqcGdlyP9rpBoB4IZg160fatGkPnXpqaWrcuKGjBR3wPmHwYOpYtiwV+AcAl61fT0/PWEx50q6kPHnSKDNzMXXqVIMuuaR1llQ7duygRx55l/Llu5JKlqxJW7cupYyML+nxx28wUvXoHmbftrCXjjnS1qsPrG77/Zazs3b7rc/LeTr+hBgHfJ9xv+cDS7+nnHIK3XLLLV4uF/uyAoCxH6J4NzAZABAjwFn1/fiH+RlBXE/djzbocq9VG8zX0GmnFz+7KLRSfQ3xO+BLvVHr9MGtDLSGxQ9Wnldf/ZmqVr2Z8uUrSHv2rKV9+4bQ44/3NEDHDnzt6tcBQJ0lV1gEP3/kERqbLx8VTUmheRkZ1CEzk6bOnUvlypVz657252Y/LTXAZMuWLTR8+Aj65ZdDlJ7egvLmPUylSq2n3r3bZ/k1al9Is6ATACK6+JVXRtD27adT0aIVaO/e3+m007ZT797XOEblfzdhAhX6+WeqnZ5uLAHP27GDTmnVivYf+Iv27fuTTj+9+gl7Fv/www/03nvbqWrVqykj4xBt3DiP1q2bQF26VKTu3btnbe2n2a3jioW5dGwXaeunXWGeY7XTRpj1e63L7E/IOV8BgaNHjyak5Nq0aROKTCZPAAAgAElEQVQ1aNDA8LXVPbBsjMjhRYsWZbkOOUUu44X29ttvp3HjxhmGhssuu4xee+21SF6guQ8CgLqjKeUsFUgWAOSN1b34hwWZMuw/V7Ro0eOsTbB2hZX+wIuPnp9l57C1Mvsawrqos/TqdRwYAEeMGEfz5p1O5cufZ1jB0J9VqwbRvfeeY0SF6lj91GuHBYAYt97XX08Lf/iBquXPT79mZNCDTzxB3W+80WtXHcvbOer/+utSeued72jOnLxUuHB1KlRoHTVufDHt3LmMLrzwCLVs2cwy6jho45wAcPbsOTRixC469dTLjctgvFatGk633tqQTj/9dNtLQ0tsy7dxyRLKX6gQVWvUiM486yzHsZ09ezYNGbKeUlPPpW+/fYZ27UKS+MJUo0ZB6tKlNvXoca3nuWHXQN1lTKvl+bgDYFz2vTZrj/sKuz4MHTqUxowZQwsWLDB8jBs2bEiNGzc2fi666CIqW7as7dyaPHkyIVMBzuvZs6fxQu907wbwoQzAEePepUsX4yUTEBrVIQAYlbJJUm+yACCWPPntHNahtLS0SEeYl1hxEYCGV2uTTuN0AZC3tUP/vfjZhQWAdr6GYeyWYqUTA+C0aTNo5MijVLly+38SWh+hLVtepGeeuY4qVaqkI/FxZbhezB07H0AdCyADDralQjJkwGiZMmU8t8ftBCsARB/+7/8+oB07GtGSJURpaQ1o9+6FVLr0SqpY8XSqUeM36ty5rWPePL87mDgB4DffTKXx40+iatWaZ3Vr1aoxdN11Zah+/fpuXfX0OSxCDzzwMk2duoZ2725G+fLhmvOpcuX1VKVKJj36aAeqXr26pzq9FNZdOgbI4IUxqkA1L21Wy1pttea3rijOs0qfc9tttxlbweFePHfuXOOnb9++1LFjR9cmTJs2jS688EJHAESOUAQZ/QxrdO3aRp34HTv/4DMkO4/iEACMQtUkqjPZAJB9z0qUKBHaW755uuAGiYcv5xf0m87EbRq6BWmoe+f68bNTgcetLXafM3zic8CRmmw36G4pdtfkMYY+//3vYNqxoxYVLlyRDh5cRC1aFKJbb+3ua+ydABBtCZoaw6/GdudZtQdLrU899QmVLduDvv12ARUqhMCHQ5SR8QVVrZpGXbuWo7PPbphVpZsFy0sONycAXLlyJQ0a9D2VK3c1FSpUnPbu3Ux79nxO/fp1juRlbeTIkfTf/y6mvXs7U9GiFalkyeK0f/8wKlFiNd12Wx264oorQkkIrzumTkvHaiCPTv5H3Wv6LZddCb39ts8qfQ6W9h966CHDAuj10AFAWBmvueYaw1qoHnBvwVxr105/z2ycj5fD5s2bZ92nMD9Q96OPPkqPPPJI1iUEAL2OppQ/ToFkA0B8keBsjijgsJZiWVDUzdvJAXTwwANoRnXY+eihHbAO8puw3y3cGHj8wDIgzG3/4KgAkNvNUDZv3kLaunUvnXVWVWrSpInvHR8SAQAxLq+88hH9+WczOnKkMC1cuIZ27VpNJUsupKuuakxXX32ZsSuG02H2uTIHPzAU4vulLrO7BYFMmTKdvv76V8rIKEqFCu2lq646l+rXrxfa1wcvBLC44vjll1/pnXf+pA0balOxYnXp8OEj9MeGwZReeDzd3LYmpdapQ606dbLNHRhao2wq4pc3jIUKhyhu3nc37PuYW9+iSOfjdk0vn1tFT8PS9/rrr5/gD6pTrw4AYptIRBhv3rz5uCph2R84cCBde+21OpeyLfPhhx/Sv//9b0JC61q1agkA/qPA39lV5fCtQLIAIO9pywBotwWZXyHNUbWoB9aW7AZA3JzhO8QBFm4Pc6f++rGW4rqshdv+wX63y3NqM/efI7CD9B/XmT9/AX399Xzat+8vatCgCp17bgMqX768oS/6iS3Hpk//mQ4cOEy1apWlli2buwKUnzmG+Yuglo0bt9Epp6RS7dpnugbP2FkkV61aRe+/P5UOHqxABw/+SQULrqZu3S6ms1x859zmitOesIAWfI7xUbfoMteJlwI40yNiU03k60cz9RxE/s4YPZqK7dpFeGgsP3aMvvv5MG3b1pS2bStGe3aso4I0jPq1rUA3Nm1C369eTflbt6bG554b9NK+zudIW9WlQHfp2AzevhrgcJIbyId9Pa/1WflOXnzxxfTll196ivLm6+oAYNgWQLXP2L8Y1kX8e9555x0nh1gAvc4OKX+cAskGgOi8eQeKIFPCLro3jOVTt3aZgxLY4hbW7iZeraWcVw//sq+hU6BFmADISyS8uwsipN2svJgH2K4NbYWPD5bq1WPJkiX0+uszKTW1NRUsWIw2bZpBNWv+QXfd1dMAQPj4vP32bDr55GZUoMDJtHnzDLr44qLUocOlbkPn6XOA00cffUmLFuWhggWr0qFDG6h69b10ww0dTmizWrHTkjRAazUgJ39+Y7/csCOx7dJ1oH14MeBlzTBgBfMe6WwO7N9PZcuVM/I9mo9vx4yhiqtXU63y5Y2Pftu8mb768wCt3Ey0YsVW+uuPZfRAq9rU9h8IXrtjB/18yinUtmtXT2MVVmEGQLdcezmxdIyXEfw4gXxYOvipx2qbOoAT/P78vFToACD8/KpWrWpEDLMPIH5H5DESUPv1AZwzZw61adOGYGG0WkYWAPQzQ+ScLAWSBQABSwATHLAweAmGsJou6nKvVRJlP9Yzr9OSIRN9wbIHrCz4PSyncV0ANGuhm1cvrL2M1f170X9Ahdsy/9SpU6l7ly6E+NLdmZl0NC2NRk+adBw8vPHGR7RyZR0qW/bvBMEZGYdp+fKB9MwzN1DJkiXptdc+oI0bG1J6+t9LMvv27aKdO4fRo492PyH/o9exVcvDYvfmm/OoSpWulCdP3n8iZEfRv/5VmerUqWNbdXb5JALAzHnXrBqFcQI4ADrNO5iYfQl1I7QBshOGD6ftCxfRkjX7adPhTGp4YT26865exgsADszPL157jS5PS6OT/4H8g0eO0JfbttElN91kPKAXjB5NHUuXpmL/7B87Z/VqOtSsGZ3XsmWQofN9ri4Ami/g5rOpgrffpWOrvXZ9dzTkE+0sp0iwDgu6lz7zHvIAwLZt2xorOpinvG2guemXX365Mb+xXIt2dO3a1bgPjBo1ylcvly1bRi1atKDnnnuObrTJECAA6EtaOYkVSEYADBJ9ii82BzY4RffqwlOQmYgHKh7yaIe6hVuQOs3nIg2C03I5+yH6iXQOCoDq/r1q/92W+QEfDWvWpL5bt1KP/PnpWGYm3Xb0KO1t25aGfvxxlgQAvHXrzqb09DONvx09mkG//z6QHn+8KyG9z8CBw+mvv1pRWlo1YwwOHz5EmzYNof79u4QWuICXlREjPqfx4zOpRo1LqGLF8sYDaPXqGXTppYepRYtmngEQ+uBhuHjxGipQIA/Vr1/zhG3SdOYQwG/ChO/op582EbY6btiwIrVpc76tVdJsOXKDFZ0Ak5nTp9OaESNo9oqiVLLwRZSHCtGMP+bRRe2LUt++PbN8ECd+8gmdtXs3VS5Vyujahp07acHJJ1Pbbt2MPHFzZs6kbTNnUq1ChejPY8doS8mSdHG3bpHmcHPSOMxce2EvHVvttaszX7KjjBU4428AQACV7osF2jps2DADvPgc1IPf8fIIax988SZOnEhNmzY1uobvap8+feirr74yygEIkQfQz17qyFsIqyUSV99///220gkAZsesSuBrJCMA+g0+8JLM2Q1Cgkwp1eKGetyWOoNcyw4AVfjCsoqfSGf46jC8emkj+s9BLnb79zqB65o1a6hJ3bq0LV8+ygdyIaJ5R49SlyJFCDtJ8PHjjz/SkCGLqVy5y40l4HXrfqAKFZbRLbdcY1ixfvhhFn3xxQ6qVOlSypMnP23cOJsqV15Ot97a1bAU+E2XwteHPm+++QWtXZtKS5aspXz5WlJ6Oh5mZ9CGDZ9Tz54NHJ3a7SyAM2bMoXHjNlDhwnXp6NEjlJGxgK67rrFnB/kxYybRzJn5qFKlFrCz0bp1U+nCCwvQxRdbW810LEcMK2wh5AAT3u6LoRA7+/y+ZAnNmjiRdixdR5nUmcqn1jCk+3nndsoss4CeeurarDxv2A5wwahRVCUjw/ABXJ03L9Vr396wRL711ijavj2FjhzZQ2fVLkGtWrc0tHDax5vHCNHLI0d+TatWbaXatSvS1Ve3o/T0dC/T2bIsf7+iyrUXZOnYaq/dwB0OqQIrcEZfEfz166+/egLAkJrkqxrAZ48ePYw5iPbjwL3ywQcfPA4IBQB9ySsnsQLJAoC40XOGeAAgrCi6fk9uy712s8nNeuZnFpq3ksOyRJQAaPaX1IEv3X75AUCMIe/p7JRE2kl7XPeMKlVoXEYGNcyb12juG0eO0Ke1a9PXM2ZkNR99nTJlGk2atIgOHDhCp52WRu3atTR268D8QT1jx35Dc+aso6NH81CFCvmoY8eLjHkGKOYt9BgGeflN1wqB5LWffrqDTj31Uvrtt+m0bNnvtGfPPjrzzKN06aU1qV271o5LWlYACAh7/vmPqEiRjlS48N+5MLduXU7p6QupR48OukNn9PHpp4dRWtq1VKhQMeO8/ft30P79n9ODD1ontNYBQKsG4KGuBpggkveHjz6i2thOcMsWGvf9XNqV2oHqV21PBw4fol8PHqCiFRZT//4dj0v0i0CQdWvWGJeoVKWKYcV9+OFXac+ellSx4jl06NAe2rDhQ7rttnpaezJv3bqV+vZ9kQ4evISKFz+NduyYT6mp0+iqqy40LMDYW9iPzxnal9259tysserSMcYR5XXvn9qTKoSCVgCIl8WWLVsSfHoT7RAATLQRzeb+JCMAAprgiO52c9Zd7rUbsqiDTXBdXCPsiGa1P6q/ZJgRxrgGQA4a61haVAjXCXJx037A00/T0IEDqcfhw7QnTx4aljcvvfPJJ9S69f/2jEUbeUkJLw1YygGAs28ZfseDEfMJv8OXbMyYH+nAgeKUkrKPmjWrRJdccoFRBwMMfletWU5WwunTZ9DXXxegKlX+jvzbt28rLVv2BV1zTVUjaMXtsAJA/O3ZZz+lSpVuorx582XVe/ToOLrnnn+5VZn1Ofrz7LPvU+HCAMmSxt+Ru+/YsfHUt6/1dlt+AdDcqEmjR1P5X36h2uXK0aGDB2nyxIn0+uJtlFrhFjpaqBSlpO2nc87dT3fccb0jIK9YsYKeeeYbKl++d9ay9aZN86hOnV+od2/3tB1jxnxFb765mdLTW1ORIpVp69a5NHv2AKpc+TwqViyTKlXaQ48/fpfhL+r1iEOuPbulY/QFLzGwgrNFVvelxqsOXstbgTOsxUjDMmvWLK/Vxb68AGDshyjeDUxGAMRDEA9hpyg2L8u9diMcVrAJIlrhb2UVbBKFldEMgNAJ8Id26MCX7ozXBUA/foZuAIiHG3x1vhwxggoXLUrX9+x5QpJYFXjRVugA8AEA/u3zdzhrSQnWwKeeGkZFirSnEiUq05Ejf9HataPoxhvPyIoKhC6qNYuXOfF3KyshgPLNN2dQ2bIdDCsbAHDXrtF0991XavkY2i0Bv/celpVPpQoVGlJm5jFavXoKIdbBbunWbjy/+24GTZiwg045pYlRz44ds+nKK8vTuec2tjwlLAD88t13qcm+fVTxnxybGKf/Tv6G1p5UkQoWLE6NGlWjNm2aGy9GquXKDNsbNmygRx/9jMqVu4MKFTrZaDOWsVu02ELdunVynMa4Zp8+99LXX++lk046nfLn30sHD26njIxrjJ1L0tNL05o1w6hDhwzq2fM63a9EVrk4AKBVo/lFjCEQ89nLS41nITyeYKUbot2x68ekSZM81hb/4gKA8R+jWLcwWQCQI7owGHhY47CyPPld7rUa5CDBJqhPBR/zLhp8PTfQCTr5ALHQBA9PtAGW07AOQC3n67OqU93JBNf24mcYRBd1DsB6cM+tt9LcH380NLisXTt6efBgw3qsAiDSkLzxxkKqXr0rpaTkMbqzceNP1KDBOurYsa2tZG4+b7NmzaOpU5f/kxz5T7riioZUr5595K96ITsAxFLop59OJuRETkk5SjVqFKaOHS/2nNYDYzdv3nyaO3cl5cmTQueccxo1aFDf1uoWFgAiYOPAxIl0UbVqlDdPHlr+xx80t3Bh6tirV5Z11W5JE8uBSNYLgEdqjvfeG0k//ngypac3pQMHtlFKynfUr99VlqlkVG0RGfrUUz/Q+vUXUKFCjejQoe9p27ZBdMr/s3cV0FUc7/fGQ4wYSSCQENxdi7u7F2mBGlYHyg8olAqUKm2pUKQtxaVIcXe3hOCEECQJIcTd/udumHTz8mT35YV/IJlzOKW82ZFvZmfufnK/UjPRokU9aa9GRV2Gu/tqLFgwU/UrU5jJlvkhSGAtSKq1+ROKjxpTRB2rEZ42jsKAgAB888032LBhg5qmnou6xQDwuVimwjvIoggAtWme8mvu1bbCxgabqAE+ptAyahu70IDy0uZlppTaRc1O1wUAuRZKyaR19WesXDS1jb07dkRVf398YWmJqKwsjMnMRLNx4zDr009zAUBG7X311Q74+Y2BlVV2Jo3g4IPo0iUD7dszSEJ50dQSch/xY4LkyILmR2iz9LWqjwaGfYSHh0uAnu0+i2IqAEgQt3fzZiReuwZbpuArWRKtBw6UCLr1lYiICBxevx6uT8mgw+3sULNjR1y7dgsBAffh4eGIzp1fkvIAGzJpfvXVb7hwoT4yMyvg2rV7SE5OQGTkbNStOx/VqmWTR9+9uw6dOoXj7bdfVy3ewky2zPdW0KFom5gu0zFlqpnFxJCc1QpOG0fhsWPHsGrVKimq90UrxQDwRVvRZzyfoggANYGHKcy9ugCg2mATQ9Gtmv0YC3R0bTMe3sLkzLFTNjT7apIkm2Kbch140dEZXxRNMmnKg5Ql7J+0C/JcwvrGoFYu2lLXMdigSa1aCLa2hsPTaOG9aWl4180N527cyAUAKbdly9YgIIDBH7WQlBQBB4dATJjQT5G5Vt9cdGkJxYWqK/Xas+IBVLoX1NKHUFN//fp1JCUlw9fXJxeZLmXCIAzuj9KlSyvaF3s3bYJvcDCqlykjDflWeDguu7qibZ8+ubgJ+RtdK/buPYrbtyNQqZInundvlyu6d9myFdi2zRW+vn0ls35SUhwCAl6Dvb07rK3b0DsXLi5XMXfuO0aRABd2AMgPB6XvIuWZn6hjpfuL9bR9ZOzatQvU2P74449qmnou6hYDwOdimQrvIIsKABRaJa4EAQ4PClIsiNy92vzr8rtqajJdyP3N1JBU59fMLJ+jtjGojZhWIzOxDgSAcuApwCaj9n78cQPi43lhEwSkYObMCXB/yuVmKgBIYEKwoUmkTYBRt2pVXLG0hKd5tll3c2oqPi1bFsf8/XMBQP5Gs/OVK1cl0FCqlAOaNKlnVACAEhlqRsYKknO5hoXylacSU9KuMXVIhXLwoD8iI+NRrZon2rRpqpU/Tw0ApCwXL96MiAgvAPTlu4G+faujUqUKOH78HB48iEHlyp5o1qyhYtLtdT/8gF7OznCwpd4QSE1Px/qHD9H5tddykUYTOM+Z8wvCw+vC2bkqoqNvwsXlDGbMeD0n4IofB9On/4rk5LYoUaI0YmOPoUWLTPTs2Q7+/pfh4GCPZs2aGr3+hTnbBi0oBH9qAKDmvlITdayGvFnbHqPplykbv/jiC2O2d6F+phgAFurlKfyDK6oAkJcjizEExkpXVQnRsTbNkxqziLFmZvkc9I1BDYhVKhdRj2vAA5sgRfhlEvytWLEWa9cewNWrt2FvPwYtWw6FnZ097txZhS5d4jBhwliDXSkBxpw3+xX5abUx/DNbSNzevZhBgJeVhSlmZhg7Zw7GjR+fBwBqS0JvcKAmqmDI7KZLS5jf7ulPt2jRftjatoSDgxsePboCb++7GDu2f56MNPoAIN+VgwdP4PLlh3Bxoa9pEm7cqAQ/P2rTsilmHj1aDjs7CyQm1oejozeio2+gQoUwvPHGYEW+qbvWrUPVsDBUesrTFxIZibMlSqD9wIG5QCRThv3yy034+f1HZ3Pr1l8YM8ZL4pMT3IRMI7hx414kJ6eiR4+WGDx4kEFmAaXyNpW5XGl/auoV1D43tIeVpA/UxlH4xx9/SIT506ZNUzPN56JuMQB8Lpap8A6yqAFAHt48DES+Wl1pfUyxYvp47kzh58Yx5gegycegK42cEhBrrKyEqZnjYFAFTc3btm3HvHlH4OIyCv7+vyMrayJcXB6jbduXEBcXDHPz37B48WcGu9QHANmfUlM75fvpxx/j3w0bJKA6ZPRovPPee5L2Qx4EwgEV1MUoNLPUWKtJ88e1I6CWaws5TrmWUIkvoT5h79lzCMePl4SPT2OpGmUbHLwWr73WCD4+Prke1QUA+czSpRtw65YXPDxqIykpGidOLEb16j1QoUKrnDaOH58n0a3Urp2dnze7r1V4/fV6eUisOWeSP3MfeHh4wMvLSwr+OLlhA8olJoL63Ls2NqjXu7f0O+v6+9+AjY0VMjOTsWWLOfz8BkkE14AZgoI2YeRIG3To0EHqd+vWHVi9+iosLQkI42BldQqTJw+S8irLI7rVfMzJhVUUAaC2fabWdMz3mkXuskLTLymcmKXjRSvFAPBFW9FnPJ+iBADpF0bQwcubl6rLUxqJghK5LpoTTTJjjsfYi8JYACj3tdOXRq6gAKAwu/KAl/MYTpz4MW7f7g4Xl8Y4ffp/MDObhNTUO2jfvgHCwragTJkT+N//3pEuWn0y0wUAxbwJEPQRSevaEyK6mJf8swCA16/fwI4d5xAdnQFXV0t0795IClJQUjR9AA1pWIzREu7ceQCnT5dCuXL1c4Z05846jBlTL096OV0ZJAjMfvxxP3x9/+PtO3JkExISrqNTp6lSu3FxYQgMXAA3t86oVKmdrK/NGDmyHGrXrp3zb3y/yBWY7O+PUmZmeACgXLt2aNG2rQQISa+TlZkJH19fyf90376D2LTpNiwtG0jgLy3tGCIiwpCQ0AtxcY6wsopGqVIHsGDBBMmfjx9277zzDZyd34GdXXYAzb17h1C//lW89daIPHmOheZKTWYYNeZyJXvBlHU4f36sqfkYMVX/hkzHfK8pbwJAYTqeO3cuqlWrhlGjtPNTmmps/x/tFAPA/w+pv0B9FiUAyMtbfBkSOBU0ANQMNpHTi5iKT08tQNP0taPmTZ+PjTHZOvS9HnKzK7Wv1HQIYmU+98EHcxAQ0Aaenh0QGroLN2/uR3KyA0qVeoiEhMeoUaMNHBwi0L17JYwaNVQnCNQ0jWvOW1dUMyNjSf3CyFhqhjSLqQGg0MJqC7IhXcvPP++Ck1NHODt748mTECQm7sf48QTILgZPISVBIELDIk+9xoY1ozV17RGCqSVLTsDVtYMUABEWdhUlS17EuHGD8viI6QKA5OP7+eej8PP7jy8vOPgsHj1aAxeXJjAzc4KlZTBatPDCwYOR8PTsBzs7F0RH30NCwlZMnjw4l88hc77eWrECvf38JJqYxNRUydevw/jxedaU+3v69F9QsuQYODjQ35Bg7jQuXfoCsbHesLDwlEi9y5ZNwLffviPlgA0LC8OHHy6Bn9/snP0XFXULjo4b8cUX7+asiz7OR01QqLmYhTnd2v8nANS26eUfNpQb/5/l888/lzS7POPq1KmDd999VxXV0axZs7B48WIpV3TDhg2xcOFC1KyZnRdcszDTCImm+R6zf36czp8/X8rlW5ClGAAWpHSLQNtFBQByKcXhwMuOmgBeosZq3pRsDRHlStOdoBcxNZ+eGoBmDKGymvb1yUSYXYWZVJBLU0YiZRqfP3LkCGbMWAN7+1dhbe2GkJDfYG9/CenpVVCnzsdwdfVAWloC7t37FnPmDEDVqtn5XzWLAIAEQadOnUVYWCSqVvWVUnwReGoWjm/btj3Ys+cWzMy8kJUVhq5dq6Bbt465qhoCgGqc44OCgrB793lERCTC25sUJI1zRYyeO3cOW7akokKF/2hkSNrcr5+TlGbMUFECALXJQRO4iAtNF4XHpUsB2LfPH7GxafDzc0bnzk0loKWpIdIFavg+Lly4GhERNeHtXQ+JiVF49GgXRo6sLX2ccI/4+vpKARXMv7xz52WkptrCySkVAwa8hOrVq+eaxtEDB1DyyBE0kJmgdwcHw3PwYCmSXF4I9qdNW4bKlafl8DcGB/vj9Onv0LMngzyiYGvrggcPjqJjxycYPLiPpOH76KNvEBvbHZ6eDSQS7Nu3V6NvXwsMHqw7nZ6aaG6+q1yHwphuzZh9ZWivmup3QVHD9/DkyZM4fPgwdu7cCb5rPBMIBOnHOX78+FxaY83+v/rqK/z000/YsWMHKlasiE8++QR//fWXFEyiLYFAu3bt0Lp1a6nesyzFAPBZSvsF7KsoAkAerDQHFzQAFEEOBJn6Ag3ys62UADQ5r6DwtVMKfJVm69A3B10RzgKQyjWAvCTpXL9ixS5ER8ejXbt6KF3aFWvX2qJSpb453dy+vQavv14yT+o2UYGHPdf4u+9W48mTKrCxKYWMjAD06FEO/fv3yDNcXhALFhxA2bLDYW1tj5SUeDx48Dfef79zLlJgthkSEiLRkxB4VKlSJactNdGR1O79+usu2Nq2lrR7jx/fgYXFSbz1Vu+cgAR/f39s2PAEFSr8l57uzp0dGDy4TB4go03+vKi53vk11enSEsq1WOyfWvWTJy/C3/8hrKws0KyZH5o2bZTTvz6tFoHY1q2HcevWEzg6WqF9+5po0qSR1m1FOXN9mW9XG5hn9Pj9NWvQ86mbAKN91967h1ZvvinlcZYXjmnu3EWIiWmH0qXrS9qbCxdW4PHj42jTZmHOB+Ldu/vRocNjDBmSvQcJBL7/fg3i4jyQlZWAatWs8Pbbr0i+Zizc22fPngXXmeZHXWZ7XbIV76cwZaqJhM3PeWLoWY6XZ8pZI00AACAASURBVM6ziC43NBZtvxMAalLUjBkzRsoEQj/QU6dOgUE+w4cPlzK26Cp0MXn//fdz/AYJ+rl3vv32W+lZzUIA2KpVK8yZM8eYYRv9TDEANFp0xQ9SAkUJAIqvah5i1OQQeBTUwco+eAELImVDplZjd6M+gGaKQBND2Tr0jduQyVsbANTWHg/sr78+hQoV3pa0NJmZ6bhz51tMn95RpyaMGt6NG7fh8GFXVKnSTXouJSUOoaE/Y+7csXlMqEePHsXatemoWLGDDGTuwtChdnjppexcvJzPhNdfxz/r1qG2tTUupaTglbFj8em8eRJQUAMACQ7+/Tcdfn7/BTncubMLgwZ55piZeNEuWrQZ8fG14OJSDk+e3IWz81W8/nrfPNGmBF8E2iJNHcdrKgCouSZyPyxhOuZHxo4dB+Hv74iyZRsjKysdoaHH0aePp5QdhEWJWdMUgTTsZ/vq1bC+eRNe5uYIzsyEU7Nm6NC9ex6NP/cgcwIvX74HUVE0qyejfPlMREQ8QVxcG3h5NUJc3EPExf2Djz7qLWmDROFYSYFDLR3/XZwlXLepUz9HYCBNgeUAXMLYse0xfPgQg6+5kC2DGYQpk7Ll/pIDbv5d6UecwU5VVBAAkFRV/x/9Gxqqtv0zePBgfPfddxIQV1L4ccH3iCZdagtF6dKli6Q1/Prrr7UCQH54cK08PT3Rp08fzJgxQ1GecyVj0lWnGADmR3rFzxZJAMhlL8gcuiKLhdhecg2XqbecLoCmGWiiTVOiZCzGAkAl5maOUYkvJtv68stfcPGiBaysqiIt7QqaNrXC+++/kUe7Jdd2LlmyAaGhLeHhUStnqkFBP2PatM6SL5e8XLp0CVOnLkZs2APY2LmhQfNxSE29inHj6kuapoCAq5L24I+v5+FEVha8zcwkYPGShQWWbdyIFi1aSKZKpQS52ebdZFSo0DZnGNTuDRnincukSc3Y0aPnce9eNPz8XNG8ef1c3HLca7t3H0FAwGNkZTFytQS6d8/Og1tQAFDbviHo+e67jfD0HAILC0bSUsv+ECVKHMdrr/WV1knQpzwLsyblQnAWExWFUp6e0npr+9gT0bYEM8HBwZLfIuvSF3TNmp24di0M7u726Nu3BRo1aqjzlWFfR46cRkpKOuLjI7BlSxp8fGbAzMwCycmhiIz8ECtWfC5poZQUeTSr3HQsADf/jfMxJsBESf+66hR2AMh9SK2pPGVl9+7dsWbNmjzaX11zpE8qI9hJQC93MRk6dKik4V20aFGeR2luJsDkWc/Uc6+88or0LDOQFGQpBoAFKd0i0HZR1AByWfOTK1bXtpAHOAiNH79ICxoAyrNpGNK6qd3ScrJmJc8KAMaLlTLQl79XKQBkv7wQqTW7dy8M5ct7S07ZciJaTW0nnzly5Bg2bIhFxYqDYW5ugaioYKSmrsXcuRPy+FZNfvdd7Fq2DG+kZSLUzByLzYDhb0zC+PGvYtmyg0hPr4Fjx5ag8fnV+NHSIgdMkJHQ+8MPMWXKFFUAkKbk337bjqysJpIJODLyDuzsLuKtt/qp4pI7duwU9u0jSXYraY737p1H5cphGDSo2zMFgNwn3367AV5eg2FtXQL0w3/yhA74xzBqVM8cKhquCy9nJZxuSvZbfusYolvh7xwvASK1ygR61H5R4ydABjU/c+euQ2Ym18AWgYHfw8pqBKpXH5ozvIcPp+PTTzuiefPsNHGGCuXJPrUFBxmKhJX7appaSyfe78KqAdQWoELTLDX89MVWUozRAGq2S9/Djh07Sh+4BZFFSfRXDACVrGhxHZ0SKEoAkIBDZExQmypM3xaSR5hS0yb8Y9QAHGO3qBygCc2jKcmtlQJATQDGC8KQeV0E41DDlp8iT+UnqF14EVAeK1Zshr9/khRJamsbhtde6yw5gssLM37Uq1oVp83N4ZuVhazMLHyfmYEDzZqh+6DXcO9eLZQuXQtHj/6K+1s+wuHMVFhZWSIjKwvNLS3x2jff4OWXX1YFANk/NQ0HD56XslqUL++Gdu0aaY081iebX3/diKystnByyo5YzshIx4MHq/DOO30k0GUKH0Cla7NjxwGcOmWG0qUbID09FRERJ9Gvny/q1s2maOFe4pgInLhm4l3UNG0a2jdKx8P2GQXKfpkuTh5sJNowBABFPfpj/vbbTqSklENWViwqV87ChAnDJRqZ2bMX4M6dVvDyygZ3Fy/+gGvX7qBly28kzWdqajQePXoby5ZNz+VPqm8eHDPloFRzrybAJD+gUABApWBK6VqZqp6m1ptyadKkCQIDA1X5wmrzAeQeoilZmw+gLgBIMFmQGu9iAGiqnVNE2ymqAFBJpgglW4IXiNwPT66VIgAUzupK2jKmDjVjvCx40SjRuqntg+3zj3Bu1/a8Zv5epZdWfgGgtshiAR4EPQ4P3zt37kiO64wk1TYPXu5927ZFCE1qIudvejo+9PJC7+EfwsamHxwc3JGUFIMf5zdAvfgw9EAWtllZIaR0aWzevVsCAgLcFCS5uKb8//prK6KjG8DNzfcpyIpDdPRmTJrUXwLAzxIAsr9jx87g4sUQWFtbonnzyqhfv26Or5jwaxMXoi5fQpHjOD9aQprj92zYALOgIDDTdKiVFer37YtqGhHDSvj2OO7//Y95ZAfD1bXS06jf9ejXzwa9e3fDW2/Ngbn5m3B0zCa+Tkp6jB07+sLTsz0sLSvDzOwE+vevhkmT3lT8+qkFgNoaVhK8I8zISgfGd5ayLYwAUFuACv+Nkf+kBlIDfOnnxyjgbdu2SZyjDO74+++/peAvzShgfkBeuHBBCgLhbwSbr776qsSDuW7dOqWiNapeMQA0SmzFDwkJFFUAmN8UakoiawuabkYceLzE5JpHU+5utq1J1SLbOxL45O80c+ji1tM1nvxEY1O29NPavZsp48JRtqwLundvneOkryQ6WoyLF3z9KlXwSUwMhltZIS0rC6MyMuA+ciReatMVJ086okKFNpJZMzBwN27f+AEOlmmo1bgx3hw3TjLxczwE4MJxvyD9s+R5f2/evIm1awNhZ1dP8r2Ljg5A+/YuaNmy6TM1ASvZc5oAUB9o4d5gfc7v0aMnKFXKRYq4FgTEvMz1XehnTp1C4vbtaF+xItIzM3EiKAh7o6IwaPx4yVdLPKskMIWa2tmz/4Gf3wc5Q46MvI5SpfZi2rTX8fvvy7F7twP8/AZLWUMePjwMT8+9aN++PiIiIlGjRjUJhKgBIILOROnHlBL5Cy0hZSsP3lEDuPkc33lq+Atb0eafKDSA9OdTI3/Obfbs2fjtt98kM26jRo1yeACpVSadEOll6PtLVoBBgwZJkeGUD/08BwwYUBwE8gw2iNkz6OOF7qIoAUC+nNTKsRibQUNonYTDP0GPLpqN/AAcQ5tOUKuICMGC8jPUBQCF5pPj5GUgd7o2NHbxu6Z8KNudO3dh6dItePIkGq1b18OECWPg7u6e06QwtxPg/fzzCly/XgalSjVBfHwozMz24+OPR6JcuXKSxo8HvjbOLm3j27NnD14bPhzlzcwQmZEBNz8/rNu2TfIz/PPPLQgJYfaYLDg5PcHo0d0lrQBlwyIuFnFpUxbikhUXrSmiONnf0aOncfHiA1BR2aCBD1q0aCxltjh//iZSUzNQu7YPatWqKZkPn2UQiJI115amS9/HwcaNO3HhQjqsrHyQknIflSsnYcCAztL7ZkieO1avRq3QUJQpWRJLjwXgelgpBMWVgEeNEujRww+9enWRulYCAHlWTJ36C1xdx6FECTfpueDgPWjTJgIjRgyUAsq+/noJbt1itK4N3NxiMHnyqDyBRkpkJOqoCShS065mXaEllINCEWCi6Uso6Ky4joURAGrzT+Q5ySh+AsAXsRRrAF/EVX2GcyqqAFBtBg0uiZzPTkkaMR6kpqabYZs0OfMQpjaEYKMgA000qVp4yPJyYv9qOQW1XT5y+dBRe+rUv2BvPw62tl549Ogf1K4dhEWLvpIufDnopPbvs8/+lbQypHjJvpR3oVu3OAwZ0k81AOTzdAug8zZ9xfhlzwuQ86W2mGZkypoUD/ydmhldAFBTa6MvilOTXFnfq3/gwDEcO0Y+MtKsZOHhw5No394eLVr8R1Uhf/55BoCU99Kl51G+/CApuIXrEBTEtG+VpOhKTYCtCVpOHT0K2yNHYGlujr9O2MDHpS0CEhJRtXkjRESswQcfdJc0NUoAIGW6Y8cerF9/HRYW9ZCZGQsnpyuYPHm45FvIQqBPOhnu0cqVK+dkhOB7oi8QStd6q6EUMuV1YSjAhO8h56pW22/KMer7aKDc5OZpni8DBw6UuP9exFIMAF/EVX2GcyqqAFCNhkgOetSkcBMAUJ7r1tilZVvybCL8Aid4KOhAE9En56Ctf2Pnw+c0AfLkyXNw5AgzQdRGTEwi7OwsYWv7Cdau/USicJCDTn7Rf/nlYfj5TcgZwr17R9GixV2MGTM0l1+mMWPUFdQiXAe0AUClflu6Lll9pjheuj/8sB5OTr1QokQ22XBCwhMkJ+/EpEk0PeYtzzMAvHjxIjZujIefnzzv70l0756OZs1yA15t8mSQ177Vq3H/tD9uhjWFlVMVOFepCh8/PwQHb8Irr/iiVq1a0p7i84Yc9VmHfmRXrtyCg0MJNGqUm45HU/qkDFq27F+EhkajcmUvvPnmYAkYKi2m4ENU2pehevIPGJ43PA9Z5G4O4u9qzayG+lbzuzb/RJpnJ02ahP3796tp6rmpWwwAn5ulKpwDLUoAkAcXQQyLkgwXchBAsCVAl5qVNAXdjLYoV3HQFnSgiWifplBTZzPRBMgTJ07Hxo0VYGHRB9bWLlL0ZErKy1i27G3JwVouf4KtqVO/Q0pKN3h61kdSUiQePvwLH37YFvXqEUQmShe7MaYqOZ2P0PQKeZsCAGrbP5qmOM0IWT7z88//PAWAJaUm4uMfIzV1DyZOHPTCAcCHDx/i118Pw9t7oEQrw6jiu3f/wejRdXORMet6FylPrtW/W7di8+YoVKv+skT+nZaWhLCwFXj//e7SR4XQ4hoCgGreeWaV+eCDn2FrOxpOTlWkaOgSJTbh119nS8FCSkphAoDy8YqoaWo15WZjuV+qPHjHVBHdSmSmzT/xypUrmDt3LjZt2qSkieeuTjEAfO6WrHANuKgCQEMEx/LIVk0QoGYFCQBpkpBHByt9Xvi7cay6gjwKMtBEbm4uqCATOSE3KRY+//wYHB2/goWFJxITN8DcfDW++GIYRo4cnseJm5xsv/++CQ8f0hyeid69G6NHj845WTl4QamJVpQDfq6XtnRXcgAogj4EOFSqAVSy/nKtlvAjJOff4cPJ8PJiQAHw+PE5dO7shmbNtKdMe541gJTRvn2HcfjwAwClkZkZjqZNXdG9e4dc9EKUzYMHrAN4e3trzT+8fPkmBAbSP485noPQrl0ZdOjQOkeTxfXjegtzfH61WCtXrsGqVVbw9R2Ws9QhIV9h6tSGkmuBkqKN0FjJcwVdR1fUtCkCTPI7dn6gcnxyv1+JvP2PP6QI3hexFAPAF3FVn+GcihIAFBc8xauL3451CLj4uzGRrZpLZyzfoNzfjQeaLgBZUIEmcn9H9pFfrj5dW5oAkNQs9K+jmWbixJ+lAJD09Ey4u9dCqVIV8dZbHhg+/L/LVN4WAQCzZVCzIj/4DQF8zfEopbIRwUPcGwUJALXJi/2dPHkW584FSz/Xr18O9erVlj4ONDNC8PfCDgApc0bY0gxLXzpt2jH6enJ9GeREjZ0cnHHv7Fm7Fhb370v/nl6mDDoNGZJnr3Iv82OB7yL7YZYHFvGBxf/yee5zfQEQSo/lNWvWYflyoHz5ETmP3L07D9OmNc1JK2ioLW2ExoaeeRa/K/WZFPIVHy/aeB/FnjUU0a10Xnw/+Ed+DjCv+K5du/DLL78obea5qlcMAJ+r5Sp8gy2qAFAbv50gUubBxEPEmMhWzRVWyzeoNsjC1IEmcgBMsxiBDudA81l+NSPadr/cRL5kyR+YMWMF0tJaIy0tCMANODhY45NPRmHs2FdV9a8UAHK+3As0uXGuhnI2GwKAurI3FMSbr6l14SUrwIzwDxVpsQpi7dTOSR4FLPH0bdwo8fSRUCTc2hqN+vdX5Se3be1alA4MRFPfbA7EU3fv4mH16ug59L8MHIbGqJlyTdMUL6Ls5WZNEYWsq236nb377gJYWr4smYAfPz6FkiV3YeHCWYo10oUVACr1mdQmG32+r2rkq0vu2ki9afplarb58+cb2grP5e/FAPC5XLbCM+iiDACp5WNwgz4fu/yulFK+Qc2gA330MvIxmRIAygGw8LcrKA2jmAMBIPsimerIkTNgbj4ZFy6EIDk5RKJ18fKyRZUqHpg3bwTq1q2bazk4dwaD3LgRhJIl7dGgQYOcbA9KMpgIrR/nyDEoMdMXJgCo65IV2hbh78p6BC3aUoRRO0aAz/1G8FuQRZ7e7PTJk0jetQvtKlSQgH1YTAz2pqSg/8SJirJfcM3+/PxzvOzuDgcbG2nY8SkpWPn4MV6ZPt1gFhoxT30p14QWS9PXTWgJ9eXhZRDLH3+QPigCtWr5YvToAapoYQqb9lbISw2Vj5K9JA8wEXLWlK/SABNt5unly5dLND0zZsxQMpznrk4xAHzulqxwDbgoAUBKXhxgPCzEV7YgMjak/TFm5ZTwDWqaHwlE1Ghs5H50xoxR8GdpyyRiSoCpbWwEgJwrs3HMnr0bJUpMwokTXyIzsya9umBpeQyurg8xaVJTTJr0Rq4mNm/ejk2bbsPcvA4yMyNRqtQdTJ36Ktzc3HSa+MWlLgis1UR189nCDgDlAhJ+ZIK6Q/OCDQ0Nxd69lxATYwlr6zQ0b14eTZo0ULX31Ow3OdjavnIl6kZEwNctm1ePZUNwMJq+9ppEz6KkrP7pJ7ROTobv01SCd588wWFbWwydOFHJ41IdY/w2KUdNGhq2pQ1kC42s4gE9rUgAqM0HVW07pq5vCDCboj998tUXYKJNO0nTL7Xg77zzjimGVujaKAaAhW5Jnq8BFVUASNAniHv1+djldzX18Q3KgzyUmB91jcXYSGNh/qQcdAU9FBQAlJua6cNGOY0YMRPR0e1x7dp1mJnNRGbmFTg4mMHScgVefdUan3/+SY4IOOcpU36Dp+dE2NpmR8UGBf2LHj3SMWBAL50AUPg2sr4xBNby9eRHhPyCN7V2JL97T5cZUbgZLFmyDUBTODt7IyUlHuHhhzF4cDUpypYXrZqPECVjlYOHI/v2wf7kSTR5ar6NS07GpseP0XvSJMWR29euXsX5VatQm8TQAPwzMlB/6FBUr1FDyXCMBoCajSvRYgmzsVKZFmYAqCZHseKF0FPRkKuDAIX8gNV0waDp18/PD2PGjDHFUApdG8UAsNAtyfM1oKIGAHkJ8WIU9A8F5dsmdgEPcuFTKN8ZglOPB5YxQETeljEAUG7+NERqnV8No+YbIZ87D3f2HxkZiZEjJ+D06TtITe0DoAssLFJhbu4OK6u/8dVXlTB27NicpujU/8UXe3LxAIaH+6NixTN4991XJU2vPIexpm8jtb1KL2P5+F8EAMj50E9tzZqb8PVlRowsZGZm4f79K6hZMxStWzcxSTCE5rrLASD37O6//0Y5RslbWuJ2ejq8O3ZEk5deUnWAch63AgOlZyrVrJkT4KG0EWM0gEraVqMl1AYoeUYVVg3gswaA2uSt6aspaGj4TtN3mzl7GVy2ZMkStGzZUiKDVlNmzZqFxYsXS1RCDRs2zEkDp60NBhdNmDBByhtM2fTo0UPKI0z3ooIuxQCwoCX8grdflAAgDw36mvGAoMaN5ryCBoCahNPC3CroCozJEqC5JdVEGhsDhIwBmNpeG20BLjxgaYadMuUzXLhQBSEhexAREYvMzDGwtnaDjU0GgLnYunU2mjb9jwCYcp08+Uc4OLwCJ6eyEmC5eXMVhg93RZcunXIBQFOC7RcFAIaFhWH58vMoV64n4uIicPNmAIKCAtC0qQ1eeaWvlH5PHsGpGQxhDGWKJtji+3fzxg2kJCSgjK+vBN6MAeX6jmhDwUAFkXNXF2BR6kvI57m/+WFoannk9zp7Vinq1I5TWFP4HEHYBx98gBUrVkggjJljevfujWbNmkk5fQ1RQ3311VcSgNuxY4ekDf/kk0/w119/Sbl+taWWJODjGbNmzRrpHBoyZIi0ds+Ce7AYAKrdKcX1c0mgKAFATlyY6Qo6uEEIWRAS8+Bg3+IA5QFhKpJUpZHGxgIhUwBA4XOpSahNAMjLbujQqShTZglOn56MuDhfpKSEAHCAq2sSHB2v4t9/f8mj3Tl16jSWLNmP9HRfZGVFo3p1c0ycOFI6pClrAg6CfSVp60jee/DgBURGJqBGjdJo06a51oviRQGAvKg2btyNwEAr3Lz5ECkpleHgkI7q1Z1RsuRtjBjRMddlJzdzyik9RH5YEbSkb08LAEh3gzOnTuHK4cPISEtDhcaN0aJtW0XBH0qPb+713bsP4fTpEGRkADVquKNXr3Z5qGZ0AUBqFr/77jecO3cV7u4OaNy4DurUqYWGDesp9lE0NFZ9WkLKuDBFcIu5FFYAyPFpjo3nCrOA0KeUHzwnT54ECcapqevatavO5WGe7/fffx8Tn/qSci1IQfTtt99i+PDhuZ7jPilfvrzkw8zMMiz8O8no+VvZsmUNbYN8/V4MAPMlvuKHixoA5MUg+L5MnadX227ioSSiMdmvIXOrMTvSUKRxfrWOajSMmuPXl1WDdTl2+uX16fMWwsN9EBV1DUlJpWFlNQAlS0bD1zcDlStfwqJF87QCZpqOCd74VV+lSpUcImACbwEADWVw4aXw88+7YGVF0OeO8PDLqFgxDK+/PjhPn3KNrjYfQAIlU2aVMLQfKDvuMW0fFIaoRCifLVv+xbZtyfDza4by5b0lgHT37lH06OEiaU50Fc6THH779l1AeHginJ2t0KpVdfj6+uaKNpb7EgoAeOniRQStXo027u6wtrDAifBwWLRpg659aPo3TTl06Bi2b4+Fr29HmJtbIiTkBOrWfYIhQ3rm6kAboKHc+vR5BeHhHZGSkozHj4/BzMwG5ct7oE4dO8ycOQYECaYuAmRzTYU/m5zWR2hdC8I/U+lcCmuGEo5fW/7kYcOGYd68eTngjKTh1Arq0gLyPCLn5IkTJ3JZHLp06YLatWvj66+/ziWqLVu2YOjQ7NST8sIzYP369ejZM/d+UypnpfWKAaBSSRXX0yqBogoAKQxT+7ZpCpiHN81cPNDVRpuq2a66AKCx1DKafRsDAJUEmLAfyodauu7dR+Ly5ZdgZzcMcXGLkZy8D15edmjfviamTx+vmEJDDnapneJhbsiMtn37Xhw75gxf3ybS1Dn2O3f+xoQJrVGuXLlc4pADQPExIdrPD0eamvUWdW/evImDB68iPj4Lrq6WaN++Tq7xGgKAbIepsnbuTIKv73/m9bt3T6B7d0dUq1ZN57CoCf3zzz0wN28CV9eyiI4ORWLiCYwc2UryvdJG/Mt/I4hZu3AhOiQm5kTvkr5l6aNHGD1rlsnA83ffrUBmZmeULFlGmkN6egoePFiGadOG5SEM5/6TUwCROHjq1E2ws5uFW7c+gbn5h0hPd4SHx004Ou7EoEHl8OGHuSPShaCojV+9eiOOH78KLy8XDB3aFfXr11e1vGIPE6TItYTChMzG5JQ+hngJVXVuoHJhB4AMKJPzt/bq1Usy3wryb0Oy4EcN65JeSv4BRJDHfb1o0aJcTTDDyOTJk8GIenmh1pEaw5dfftlQl/n6vRgA5kt8xQ8XNQBIMCYchk1h2tS1gwSnHn8nQChIh2BtVDOm5DZUamIWslATYMKxcx3GjPkCmZlTERoaA0tLCzg5BcPbeztWr16o2FQuzMziQicgUyL3zZt34+xZT5Qr999FHRS0Gm++2TgP8DQ1ACS45p6kLyovcqWF2TFWrDiJkiVbwMmpFJ48uY/U1FN45ZWOOVG0SgAgPx6WLz8AS8vGcHYujaioB8jMPIeRI9vrzVtLJ/utW6Pg69syZ8jBwafRpYsl6tSpkwOk5QCG82RZtWABemRmouxTcvHU9HQsevgQr8yapdXHSqlM5PV++WUt4uJawtU1myQ6OTkOkZEr8dFHwyXTqijaNIDU6nz88REAnfDgwQ1YW09Baup9lC6dDju7fahZ8yKWLPkyz7D44TB58qe4eNELbm6dkZhIWa7BN9+M1wumNRuSA0DN3wxFxBa0lrCwpqijnLSNrV27dti7d6/0IaikFGsAlUip8NRh9H9xyYcEijIANEazZUjUcpOnINZVCkQMta3rd7lfmqmoZeR9EQCKrCD6xqjZN83dhrRvHDsP7ldfnQV39/mwsXGVunj06CgqVNiF77+fZVAsch5DYWLnxX7+/HnJ/4eO3PrGcevWLfz++yl4eHSFvb0bQkMD4OBwDu++OzwPOTTHykKNkQCD4tIVYEcJoTK1hwcOnEBgYDQAa7i7p6Nbt2ZS8IWScuHCJRw8aAZf3wY51Wm67dnTLSebhhIAyIdpFjt8OACPHiXC09MOrVvXkXyeWHghCr9N+biofdy0KQzly7fL+efgYGoO7VCzJjkc8xYRBXz6xAk82rgR7cuUgaW5OY7dv4+EJk3Qc9CgHC49Q/vGkIwuXfLH6tXX4Oz8EiwsrBARcQYdOjigc+e2uR7VZjYkuO7WbSRiY7siOtoCWVkDKAnUrFkBUVE/YcgQYNq09/IMgaB49OhPYWXVBba2bvD0bIOIiEPo3DkE7733lqEh5/zODyjuX0PBCuIBERErD9rhbwWhJVS6pxRP1oQVtY2tcePGuHz5sqqsTtp8AJlCkLnKtfkAkmbm0qVLOWZm/p2k9Hfv3i32ATTh+mprqhgA5lPARRkAqtVsGQI/IqUYzRCCwoFaKR7mSjRRxi6lAIACzKObPgAAIABJREFUlPBCUJrZQkmfhnwM2YacX08Nr6IY+5Ilf2PDhkdwdu6JtLQ4pKRswOzZA9GiRQudQ9Q0cQs/OJo1x42bhrCwTJiZpaBp00r48cd5kglHVzl16gz27AlAfHw6fHwc0a9fWylvrGbhJSNyAFOTpMkBJy5eOSmwNjBz8aI/9uyhj1pLWFhYIizsJpydr2LYsK6KNJ6XLwdi9+5klC//n+k2OHgf+vXzydFaqrmsOQ+uIbWnHC/X/MCBMwgOTkRMzBO4uGShRYt6qFq1srS3COaWL9+F+PiqcHUth5iYMFhY+GPUqI46NYci4II+bEf278eNo0eRmZYG73r10KZrV0kzJ9LZiewPQo5q88VyPgEBl3Hy5HWkpKSjYUM/NGnSMA8Q0GXSPH78OKZNm4/r16OQkdEe7u4NYWPzCKVKHcOff36aA5Dl+2PWrLlYsuQ6bG1HIivrIWxtr6Bs2eZo2TIIM2e+q+RVk+pQBpQv5WxMKUgtYWHNUEI5aXInUg4EgDTnqgm4o58fo4AZLEIwOGfOHNDUS4CvLQqYZmaeCYw6Zp/0OyR4/+eff4xZPlXPFJuAVYmruLKmBIoaAOThKkxRSoCNkh0jwI824CUib5WaIJT0p1mHB5/wuaL2idq6/GpQ5H3oy2Yip5Uxpm+hRSN43bp1G/buPQ8HB1v0799eom3QVeQmbl6UBN0s1Ob16TMOERGV4ezcCS4uLRAV9RXGjPHCRx99oFe8XCtevDy8NeUnwCbHy8tE+LkJehQ2zA8A/j9BizYfODmYWbt2D2Jj68DFxVsaE9sPCdmE0aNbSuZgQ4VrvmrVfiQnV5dMwFFR9+DmFoKhQzvnaC3VAEDN/rZs2Y+7d70QH2+LGzfiER/PaMcE1KlDcNxakhEDcE6e9EdISDRKl3bESy/VgYeHh86ha0bcCh9KuUmWD+eHQ8+Q3DR/1+fTxveaWuSjRy/h+vUHqFjRHUOG9JIiSq9evQcPDyc0bdpQyjxDH7A33piP27cHIj29BkqUKIPY2N/h5LQFP/44Ea1bt1Y8tPwCQG0dKdESCtCta6Bso7DyE2obG/+N1FH8IFR7Hs6ePRu//fab5KNM6piFCxdKmu179+6hRo0a2LlzZ87HKS1JjBj+999/pX4ICAkg9X1wKt4MBioWA0BTSbKItlOUAaCSNG36toU2XjvNg4aXiOAbLIgtRg0jwQD75YGjxo9M6Xh0yUmAW14chiJtdfUlTKpnzpzBunX7ERubiHbt6mLYsIFac9PKg0sI4P/5ZysuXLiFmjXLo0uXdli4cDv++ScFDg5vIy3tMmxs6LhPoPUlDhxYr3TKueqxn4iICEmzJdIFUhNA2XN9hcz5d+4JEQXMscovXqHd4lrt23cSwcE+KFMmO9AiLS0ZERHb8NprnRX7wRGAnTt3BWFhcfDxcUGDBjVzXTrGAkA+t3TpPri4dMKRIzfg4lIfKSlRMDc/DR+fUmjZEhIdippCc9jJk4FISspEzZplUK9ebUW5lwU41sWhp5nfWO1FrzaoYenS1Th1ihr22khJCYOz8zV88MHLUkT0jBnbUbLkewgIuIH4eO4NfzRpcgpr1vyiCoBwH/FjwlgNoJJ1MaQllKdcEzJ9HgCgnDuRe4YfkcYAQCUyLAx1igFgYViF53gMRRkA6kvTpm9JeRAK8KPJa6f5HC99mppdn+YrNdVWkfsaiihAApSCKJpyyi+tjHyMBBukXJg3bwccHF6GlVVJREZuRceO5vjf/3Ln75QHl/BSGjXqbQQFVUaJEm2QnHwGVlbbUKfOe9i16wFsbEbBzMwBaWl/oUSJeNSrdw5r1+aO4DMkK/a3f/8RHDx4E1lZ9qB7Xrdu9aVIW4I8AfjkQI9/F+Te2sxOAhDS727durMAqsLa2g4JCbfQvLkDWrVqarI0bMYCQALbJUt2AmiCCxdi4e5eC3Fx9+DgcBV+fhXh53cPnTopz9hBrcm6dZdgZVUTtrb2iI6+iXr1MtGpUytDS6Dzd01AaCxRNWVEszfNhFev3oezcwk0alQHpUqVytM35zFv3jb4+IyT/ApZmH6wb18L1KlTEwMHvofExNfh6loJpUq54MmTpZgwoSJ691ZHBcJ9JYjijRaQEQ/q0hLKwaAApmqBthHDUfWIOJPkAJAfbtTGUYv7opZiAPiiruwzmldRA4A8KAQvn2aWDiUiVxtda2rCabkGTOTv5aHMcSl1GlcyT3kdkc6O2i8R3cxLU5D/qm1PXp8amKlTv8Tdu13h4ZENCNLS4hEa+i5WrvxMCorQFlyye/duTJ36Lzw8/tOu3Lw5BNWrd0Jmpi8uXHgEc/N6SEtbCyenXVi0aDY6dOigeKhcNxK6rlxJ0NMTdnYl8eRJCBIT92DMmK6SNpAy4B/W5RpwX3FN5MCPYxcAXRMQhoeHIyDgpqQtqlTJI8d3j8+IS1ce1al48E8rGgsA+fjJk+dw6FA0rl7NgpVVeQDBaNiwEhIT76NbN3dUr66bIkZznNu2HcTdu35wcSknySIrKxOhoZsxdmwHvZHGauYr98UUwRBK5EgZ7d59GCdOpMLBoaak6bSxuYy33uqRx5xNTdLChZfg5/dKztDu3z+NRo2CcPbsBRw8+AiRkXwXy8DaOggDB1bHnDmTFWt0RaP/XwBQU976NNhyzev/Jy+hGLO2yGnye77xxhs4dOiQmq30XNUtBoDP1XIVvsEWZQAosnQoMbVoghBhCjS0oqYEgLwYOGZNQmn6rRF8FJQGUJhp2S/HICJtTaEF4HzefnsOIiNfhptbdkRrZmYa7t2biD//nC5dwuxfaNZ+XrgQSxYsQNSTJ0jMNIeHxyj4VPoKlpYlcffuLHh7h6NFiykICnqEs2cPwsHhGL78coJi8Cd8/Tiu/ftP4Nq18ihbNpvhn/O/c2cLhg2rLEX3URasz0JZ0A9R+CLy34TJl8+xiLriwuQzmqBQfunKtVysq/bSzQ8A5NivX7+BAwfO48qVUHh4VICzsz2qVLFFp04tVEVVbty4D48f14Cjo2cOGL53b7Nif0dD75i23zXBi/Ar1JRjNgn4HpQt+wqsrGylpkJCTqJ58yj06NExV9PUKM2atRSOji9L6QfT05MRHPw3WrWywOLF51GmzAKkpkYiPv46IiM344MP6mLIkMGqh893mX+0BRyobsyEDwiQRe232JuCUssUHyz5Gao2v0mmbmNOX/rmvailGAC+qCv7jOZVlAGgoTyhYgl4GAumdzURruLSz2/GEXmghTZCaWqf+KcgnI7ZtyCzlkc3m2p7cg3++Wczfv/9Fry9J8LKyhEPHmxEtWr+eP/9sZLvHTN8cG4zP/oIO375BTNSU0HYNVsahAXinNvCt9pSREe/jfff74Xbt+MkPsH4+Pto1aom+vfvD09Pz5whM1CEEXtc10GDBqFu3brSb5qBJSdPnsWBAxbw8WmaE+0bErIeb7zRQqKXIfCmRpRaPwILEfghEtITsFFDyMtR00ws/l8MSpeWUOwhOcWHuHQN0XzkBwDK15caYFKjcO/TNKoW+F+9eg1btz6Eh0djyQQcHn4dZcqEYODAzqrbys++EyZOOXihKX7x4rPw8xuRY3qPiLiB8uUvY8SI3nm6O3/+Av7++xBSUqiZjkKLFt5wc7PD/PlX4O39v5z69++vQf/+YZg06U3VQ+a+5MeFEjoh1Y3n4wFt9DRKgXZBawm1+U2ePXtWIm5etWpVPmZduB8tBoCFe30K/eiKGgAUGh4ujCHNmZIgDyULnJ+MI0ry9xYUABQ+d/wvgUxBaBgJALkOa9b8g61bTyM1NRM1anjB2joLJ04Ew8zMHqVLM8fvUPTv3gO709PQCIAlgAMAaIyLNDNHmQrNMWHCUIwb95pkuh0z5kMkJFSHhYUtbGwu4Zdf5kgRgfv378fAvn1BryzbzEysNzPDT7/9JoFEjkWY1XlhMchi8eLtSEqqLqWIo+9azZpJ6Nevs1SXdQTdj9gHAmQIwnERcS7AoPivuDi5x4T5UoA9/lcekalNS6gZKSvMnXJNDD9aCCIKIjBIyb4XdTjW06fP4/jxIGRlWaJ8eQd06NBEMTmvmr7U1KXM+HH2888bkZnZHq6ufkhLS0FIyHb06FEScXHJT7WfTujQoVFO+jcCYmoO+VHCDwGCyFGjPoaz8+ewsyuPtLQYhIZOx+ef90VLRsyoLIUZACqhp9EGtMWeltMjqaFmMSRCbWbzgwcPYuvWrXmydxhq63n6vRgAPk+rVQjHWpQBoC7gJDcDmsLXzZiMI2oCLUzNNSg3d4uIVo6nIHwM5SCcFyspFfbvP4BffrmKcuWmw8rKCeHhexAa+ilCbp/CVWSCuR2YM+MKAOl6tbLCxl278NJL2YEJw4ePw4ULpFMZIQHXqKhNKFt2JbZt+xsv1auHUdevQ7CyMS54krMzzgQESFyN1HIKQMY5UwN5/nwgIiOTULmyh8SDRw0Y/cHWrNmMhIRk9O3bCd27d9eqzRJtCUAoNHm8/Dg2+YUo71cARCVmY85ZF3WKvJ+C1sKI443vFbXG/GCQU7xwbTkfJTQ3z+qo5J4jrcu6dUcQF+eArKwE1K3rgocPH+P6dS8kJDgiMfExvLzuYMqUAZLpX1vqNeZU/uGHDcjM9JE4AHv1qod33nnLKPDN95n7pLBpAI2NTn4WWkJtZnOCv3PnzuGbb755VtvpmfdTDACfuchfrA6LMgDUBpzkWi9BpqzW5KW5Q9RkHDEGfJqSa1Bu7ub8CVKUmsqNeTMEACTQFEE5M2Z8gxs3uqBUqTZSk5TJzp2dYGcWhS4JgfguK13SANK4dsXcHDZVq+Lo+fM5AKxOnfawt18Fc3Ovp0EaNGF2wZkz/8LbywsX09MhQhiY18MBkFj7aSYW2gv+V04+LPwvCaJOnz6NN9/8GOnpI2Bm5ggzsxWYMKEr3n//bUUiECZIOSgUvmlyUCjXEgpNoQCE7MiQ2VhEuIo5FbQWhu1fvXodhw/fQGpqCVhbJ6Ft22oSaGbRlnZNkcAKsJIgD+a+Z1AONbqU25w5W3DsmAtSUxkNbIn09P1o3DgYdnbu8PPzxLBh/aSgHTm3IzX9dC8ggbjS3LPapsZziestPr4KcPqqmjZlcIohLaHQZCslANemNaXpl5pacvq9qKUYAL6oK/uM5lXUACDFSg0Fixw4yf3sqLVQksZM6RIpzThiLPg0BQDk/GkypGw0CZ0NmcqVykFbPbZNYMD+KXPKfs6cb3DwYBV4e/eXHklPT8SBA53g7f0mIu/9iLg4f2QiQ9ICVq9ZE3+sWYNKlSrlNN+v32jcutUfTk69pbzCsbEH4OKyAHv3rkX7Zs3Q3d8fM5/W/hPAx6VK4WpwsPQvImBDXDxCG8pLmRcyNYS9e4/E1asD4OQ0THomJeUaMjKG4ezZPQa1pJzv2bP+uH49HA4O1mjUqDIqVPDL8R8UoJD9ys3GwowrtISaZmMxXmFWE+TkghvSkBbGFDlkaTJfteo03NxaS1HTCQnRiIo6jGHDmkk0SNrSruVn7xj7bEhIiJQflu8b3QJq1aqVKxjn9u3bGDx4AaKi3oCDQxMpavnBg5kwM7uAGjWmIjk5AI6Ou7Bs2XzJJ1LsGbk2V5uWUOl4+Q5yvQobACzI4BT5/tQMftIMMNH2Qa4NNNP/j+WDD/QTwCtdl8JYrxgAFsZVeY7GVBQBIA8LHjiCpJmmTaF9ElovUy6hIQAoN7kSACmNMBZjzC/ZtKB20UXoXFA+huxXRBjLSawDAwMxadK3APrDxsYTsbE7UabMbdy+bQZb21FITX2EhISNGD26CaZMeT/PUp08eRJvvTULSUmtYG5uDUvL/fj22w/QsWNHHDlyBAN69UIT+u9lZWFvRgb+XLFCMuHq0/rJff0aNuyIpKQfYWtbX+o7KysdUVH1ceTIRokjUF/Ztu0Arl93hIdHVaSkJCAu7gIGDKiV6zkB7kTWGrnZWA4KRXCJMP8K7fGZM/4IDAyX9neNGp5o0aKhTlJtEbgiLl2OXSmQofbs0qWruH8/BqVKOcDWNhNnzzrkyk8cEnIWnTrZolq1aoUCAJJw/K23PkZyckukpcUiK+soPvxwFEaNGpUjI+7L6tW7A5gLO7tWSEw8jydPvoGFxUA0btwbVlaWCA39Am+95YKxY1/Nkw5QRH8bm85OfKBqZkgx5ZlkTFvP2jdR/h5oo/aRawnFmS4Hzd9++62Usu/11183ZrrPxTPFAPC5WKbCO8iiDAB5oNFXiV+UBF2CwNfUq6Uv5Vx+IozFOI0lmxZ+hhyDvvmbGgDK+6VGjf1rpsoLCAjAmjXbEBERi7Zt66JPn14Sn9fatbtx7eolRD+4CZKr9OzXD7M++yxPBPSdO3fwzz+bJI1d586dULFiRQlssm+u+aZNmyRtZ9++feHrS6/CbCoX/tGm9ZNrHd5/fxr++ccCzs5fMlwDsbHL4e29HAcObNLr80VXgKVLD8DGpjaSkhLh5OQizb1ixQfo2lU/KbI8clVoCbWZjc+cuYBjx5Lh4VEL6ekZuH//IurVy0CHDi2leRkyG8u1L3Igo0lBw3Fv2nQQjx+XgbNzWcTFRSImhnxrfqhS5T/6lODgQ+jVq7QUQFEYNICDBr2GGzf6wczMCfHx0UhJ4bu/G++++xJefbV3DvXKwIGjcf58aVha+iE19Q5iYx+gRIn/oXp1OgxkIibmMAYNisC7747HzZs3pblxjtR0UoZcG/maiX9Tos0qrADw/9s3UZ+WULy/chL2Tz75RMoFPGTIEFMf6YWmvWIAWGiW4vkcSFEEgDxgaYoT1C50/i/ISEltqdRMFWHMXce2CC7oXK/EX1HTz5BaT30ReaYKMmG/wlxNXzf2SzBDEzDXQElZvHgxfvroI8zPyIC7mRm+IP9e69ZYqSXxOn2yqFkU/nvCtE/NFbnBgoPvSRkcSBDN6F8Wjk9XhK8YH4MGRo4cjzt3EqVsI3Z2j7B06Xdo2LCh3inQx2z69D+RldUSlpbuyMh4iJIlo9G2bUn06tVOyfRz6ggfKrmWkP+2atVeWFu3QkaGGW7eDEVMTCqSk/fhjTfaomHDbLobUVhfHyeh2FtyChoBZJj7dtu2B/Dza5cDnG/fPoH09ABYWTWEo6MX4uJC4ekZjn792klAvDAAwHr1OsDaeiGePPGHtfVwZGZmIClpAQYProzBg93RuDFjzCFljxg/fg4SExsjIyMTYWEb4Og4CLa2lZGRkYL09LWYO3c4goNjcfcu+QOdEB19GmlpEYiNTYeHhx1GjuyNTp065dAAaQJCIX9NcM2zie9xYdMAFkbfRKElFGZzrh3z8hKU0xeT5v3x48dL/ppKzkZtL+H69esxc+ZM0HWgfPny+Oyzz9CvXz+d7yuB56effip9TAirAjOSkHrK1KUYAJpaokWsvaIIAMlpxkuNLyjBALVPpqQk0NxC8lRqchBkKI2c0q2oBgBq+hnKiYt19WcKAMgxUgOnSSSt1n+xdf36+PDOHQx8CtgeZ2aiSkYGzgUGwtvbO9cUCACzM08wd6u9BPL4b8OGvYngYA9kZtaGhcVBdOrkg2+//TyHFkj4+um7MChH8owR1NCPTAlpLy+lefOOwMKiC1xcykom4JCQpfjooyZo0qSJ0uXWWk9wtK1atRtmZs0QEBCKjAwf2NpaIyFhH6pUcULv3n6SlkquSRF/Z6NKAKEAnvST27nzMcqUyR4335+HD6+gQYN4WFpaIzw8HqVLO6FGjSo5OW3V5t01RiD0Q2Q/jNbVtn6MED9/vgbS0mrAxqY3EhMvwtn5OPr1a4wGDULRs+d/2WKY+m3Pnr3SR8GFC9exe7cdzM0bwMwsARUqZKFSpatIT2+FSpX6SHQy1ConJZ2HuXkvpKXdhYXFSrz8clvMnPleHn8+Q9osypPv5rOK3FYi68Lqm8ixEzRTVnzHCdToBkLAxY8uvnfUzDIvMMFb7dq1lUxXqnPq1Cm0bdtW4hIkiNu8eTNGjBiBo0ePokGDbOJ6zUIAuG/fPhw+fFhxP8ZWLAaAxkqu+DlJAkURAPKCEICPB3dBawCFfyG/6Nm3qbNp8DIxRDadHz9DtSBN/mrJtY1yjj1RR23bjatVw5fh4ehqyThgIDErCz7p6VIUMMGNADIiuISXqMgPevbseXz11W84cCANrq7fw9m5JMzMkvDkyQAsWjQF9erVy8PrZ8pjIjtdmBliYmzw6FECrKzMYW9/D6+/XjNXEIvaPoXWkmA3KOguNm++jaAgd7i5VUdMTACqVnWAvb0DypW7j5YtG+TQz4iIYwFG5L6EHAM/XHjp8wNJgBExNu7j1asPwdKyIZydvRAb+xhRUSfRu3dNuLm55fAYyqNkqVllO+zX1IXrPWPG59i+/TAyM81Ro4YPvvtuTo55X/RHjsixYz9EaKg3LCx6wsIiHEOGdIOZWSCGDfPVCQ4+/vhXWFsPQmJipqRVtrW1wr//jke9epPh6VkdFy/648KFLCkDiKvrSElTGBc3B56e9/Hxx93QpUsXg1MW4Fr4s/H/Wf6/s2yIgRdW0zTHpy3CnEBtzpw5EpE8NbrMOU7id+HyYXBBAIwZM0bK5b5hw4ac6uQM5R7//fffiwGgEiEWYB2zAmy7SDRdFAEgL0wRuSfMhAVxKYkNJDRf1NIURDYNAQB1AVkRESrXhKnZ3JQXwYBa/jYl2ka1ASyfz5mDAwsWgNG7bmZmmJmRgfNVq2LviRM52Tg4VmEaEsEl169fxx9/XMDZsxdx5Qq1P0NhZZUCNzdnREW9h9mzG2PgwIFGm4m0yZOm/wsXriAkJAoeHg7Sn4MHo+Dj0xaZmVnIyEjDo0f78PLLTaULRW3hHHnxUctBOXJ92M7Jk6exdOkxuLjUQvnyPihXrhrCwq6jbt1ENGxYR6orTMfCL1AANfEenDlzERcuhCI93RqOjqlo06ZmTjYVoZWiGfjo0St48iQD9vZAixZVUKlSRZ2chJwf2xf5ko01ycnlxPeYGp8ffvgVmzeHwM3tJ1halkJU1EJUr34CGzb8kWdNaQH4/fclOH8+Gp6ejeDsnIHate3Qv3+XHFcAzbX46acVePSoGTw8sgmEoqPv4erVL+Hk1A8VKnTExYuXcPJkBOLjD8LLaybMze0RH/8pfHzs0a2bGT78UHlGEK4p14Nykpv6BUA3Ji2g2r2lrX5hBoDa3AtopmUksPgwNEYG1PLRh3Dq1Kk5j8+dO1cChLQAaCvUAH799dfSxyT/kJ/0888/l8zHpi7FGkBTS7SItVcUAaC4ALnU1JwxClj4gJl6+QXAYbsF2Y+2bCMCIFA7oi2FnNK5qgVp7JeXBQ9lQ1HNStoWmhBefJzLlHfewdq1ayUQ06h+ffzy55/SV73Q+om50i+SMudzGzbsxPXrFRAaGojNm3fB3n4RUlNj4eKSiOTkV7Bhw/dSpKqpCkHz+vX7EBlZTjL3UkNmaXkFXl62CAoixyD9NSPQrFlpNG6cHU2spghQf/FiIAIDGcjkhqysJ2jUqBQaN673NI+xJZyd/ZCcHAtz81vo27dJHhBPUCEHhPz/bNNnOMqVewm2tiUQHR2OzMwLGDSoTY5JXa415AeO2NvaXCkEGBe+beJZU2i2Dhw4hjNn4vHHH0eQkFBbythSsmQnhvQgKqoNdu36QycnH4FgcHCwROXC6G19biA0Iy5efBCZmXVgZmaJjIxL6NbNFxs2HMW9e15IT7fF9es7kZJSA66uHyA19RSsrLbAx6cmRo92weDBAxQvrz6+RE1AqDQtoOLO9VQUZlYlbiOm6E9NG9rcC+jbu2PHDq0fV6NHj8aff/6ZE/Sl2RfNvswaRHqpyZMn4803/wPwv/76KxhhzFzD2gpJ4kmCzj3FjyQ+T5M0tc9KXEXUzLsYAKqRVnHdPBIo6gBQDUmzmu0jB1/UehCEFEQqNTEmzWwjSlLIKZ2PmihjofUjkBB+d/r60dc2gcny5auwceMhpKWlo3v35nj99Wy6Dh6s165dky5vflmLiFUCEcqb8ufaCo3T1q37cOWKD9zdK2H58ncQEvIE6enlULLkZYwZ0wVTpryXZ5g0/VC7RHCpNs8yn9u06S58fdvmtBsScg7t2lnC3d09R6OqVvMnTPk0E3J8//57E97ebSW/u/T0VDx8eAj9+tWQLr0bN24iODgSTk62qFGjgiItI9s/ePAUbt50h7u7ryRXltDQY+jdu6LkZynkK4Cd+C/ryc2WAlCJ/4pLWvhlyiloRHCJZqowfVpCArg//jgOb++eWLBgKR49ag0Liyg4ObnDyqoMYmO74fDh9ZK8tRURjS5cBAy9DwTGmzfvwJ07YahSJdu/8uDBCERHWyM19TEcHEJx794D3LuXAkdHH5QuXREVKsRi3rz3Fcle9K+GMFvIXh5gwn8TWkJT8DuKcRVmAMiPEH5syi059Pm7cOGCZHXRLNyLnI+uQoUAz2tjNICabfJdpXWGmUlIRWXKUgwATSnNIthWUQeAhjj6jNkScl49fvERyBCQFSQAFECWBz4vEGrgBLFyfk1tAqTpizLW9DFUSqStDwD+9ddK/PZbINzcxkpRrbdvL0TDhtGYOvVtrF17BDExnhLocXYOw5tv9peAmqBxYbtcB6HdoqZn5coLcHBoARsbBwQEbIC9/W2MG/eq5PelKaO//vobX3+9DOnp1A7HY9q0NzB0qHI6iaCgIPz7bzh8ff/LBXvvnj9atkxHvXq5o3GV7jERMS1oi6iVOnAgHT4+/7V3794ltG1rmS9t5rlzl3DunDXKlq311ASZhpCQPejXr7a0h0WwiPAhFB84cjAitHxyQMg9yctYm7Zdn2ZLHiUrXyfKeOPGB/D1bSf5eK1adQCZmVVRosRjmJmdRPfuJaXgHl1FAEClKQ537tyH7dvDYG9fH/HxoTh1ag1at54DDw8fqYvS0NVyAAAgAElEQVSgoG3o1o3+pY64fj0YHh4uaNWqhWrXifxGS8tlKSK4OT6l/I665CVM04VRA0gASM2/nDCdFDBXr17NV4AffQBJ48VIYFEM+QDqAoBbtmyRosJNWYoBoCmlWQTbKooAUPg/cbn1cfSp3Q66ePVMzaOnbVwEsrxY2ZepootFP4aijIU5kvXVEmlrts3UTdu27cPdu4+xd+9BuLiQa68MTh47BvvMKKRlzYKFdSQ69FyImjWzzWqhoWdRt+59jB49KMf/jP8ueP0E5U5g4BUcP34N0dFJqF7dE61bN5ECHAS5stBUkYNw4MD3YG7+HRwd6yAj4zqSkydi/foFqF69uqJtwUt85cr9MDdvCFdXb4knLzr6BIYObSxpLdUUYVLn2sojlKmR2rIlGD4+raVLLtsf7jD69PGTomCNLdxLmzadRFpaBdjYOCIm5i6qVyeXYHauZaFtUprKju+bIBEmWBRyzi8nIX0sly07BA+PbrC1tZd8snbuXAJ7+yAMHtwdb745Vm8+XTUAkADjk0/+RKlSr8LWtiQiI29j+/Z1qFixN+rXrynJJTw8ABUrnsP48S8bK3rpOVNHSwtgLqfzESBeboY3lHZNjWYyXwIw4mGR0k/sLc6PAJBWgvx8ADMKuF27dlIUcI8ePaQoYJKGk1BeVxTwunXr0L59e0nrS/9cmoAZNcxzheejKUsxADSlNItgW0UdAGrj6FO7DXjYCK2f4LeT+xOZgkZF35gEiBIAjF/o+Tn0NPvSBQBN4WMob5sUHlOnfo+YmGZwdPTDnj2TYW//HhJirFEmNg7lzePxOGsmumQ9xF8etfDK+OMS6E1KikJMzJ/4/PPxebJ5yCNk5RlWdIEYrt+ECROxYYMrLCyYQioVLi5OsLb+CVOmeEtRgUoLwezBg/54/DgVjo4WaNmyKipX/i9lnb52hBmPl7bgq6RWVc5Xyd/27DmGW7cspYwpKSnhqFw5Ax07vpRvXku6FFy7dkcCy76+bqhcuaJOP1mhcZIDQv6b0Djx3ylvyp/yFeZ6EYilz2wsl5EIgpCbjv39L+Pw4fswMyvN0AzUrGmLrl3bKIo0FtQ5mhpAjocmfPbHOezYsQcPHoThxo1YNG/+tQRgU1LisWnTfDg4tEL79p2ldHG3bq3F0KEu6NBBHaej5j4wNQDUts+0yZL1xJoJrav8HMmvZlLpe6O2HteLAF2erYfzIz0TNYD5PQsZ8DFjxgwpXzjdTb744guJQF4Ucg0y4vijjz6S/qlPnz6Szx/HRKtJ69atJV7A/ASj6JJJMQBUu1uK6+eSQFEEgDwcCAxY5Bx9xmwNuc8bDyBt5hG1VCdKxyE0Q/wyZxE5fJU+r7Qe+9GkmTGVj6G87W3bduCPP2JRocIIaWiXLy/DuXPHkBDXDO3MrRCdtRM9rPbilawk1MoA/vdJJCwsrPHwoT+8vc9hwoSXc2XzoFxElhMCRX0XgQAxNCt27DgAMTHtYGW1SErzlp4eDFfX2Zg/v4tEI6GmCC0T10YJ2TjlfOLEJQQF0XfPGg0bVpCymOjKUkNwRbDy5EksXF2dpICHgoxoVzJ3oXHihw//iCK0TcJ0rC2VnQCDfEZopPhfbQEalC2zw+zYsQ+Wlmbo2rUT6tevnwvE6MvJqw0AMphq+vR5OHfuLlJTmeIvBO7uA2FlVQEREWtQrVoDdO06CxkZ6Thx4nukpt6Hl1dzZGVFoWpVM7z55tB8O/pr82dTIvf81JFrCYWmUGgJBRjkWuoy4een7/w+KwCg3JeTYLVz5864dOlSfpsv1M8XA8BCvTyFf3BFHQAKjj610VlqfN4KAgBqAk+aBwlyCiKBvABpdGTmRczDlf0JwJmfL2w5AFyxYi22bHFB+fLMwwpkZKTi4MHXEXTzKMpnZGC4VQLesc7AzrQ0vGNlhx5DyKyfDlvba3jjjS4SUGLRpfVT8jbya3/6dKY4u4P09I4wM2NWja1wd9+KQ4c2SV/0mvl4lbSrpA7B3MqVOxEZ6YeSJb0RHx+JjAx/jBjRUrXZWPRHQHPhwlWEh8fB19cVdevWkCJ2C7IIzbAA3wQNas3GmpyEHK/cbMl9+P33C7FkCSNze8LMjL55h/Hnn99I+4DvB907+IHn4eGRw30oACH3rHiHAgOv4ezZOzA3N4O//wmcO1cWXl4fIigoBI8erYW7ezSqV/8aMTFBCAkZgc6dB8PGxgK1azuic+cWkpmPMmXEqBKQb0j2mv5shuoXxO/Ch1NuNhYaW31awoIYi6E2tQXzcE1effVVyfT6IpdiAPgir+4zmFtRB4AEMzzs1PhmqNV+KaE6UbrUuoCnKUzZ+sZAIEEZiTRp/LspLjs5uLx8+TI++WQrypSZgBIl3BEVdQPx8YvQqpUPvpo5E8OfRjeuNDfHVIlXqyIsLMxRs2Z16ZLP9oELkbRO1IQZk9uZGqVx4xbCzu5bPH68DElJt2Bm9hDvvtsRU6ZMzkOZok2rZSwg5thXr76G0qWzTZgE9Pfv+6N58xQ0bao/zRznTCfz48dJiF0WQ4YMkgD6ypX7kJBQGY6OpSTuutKlQzFkSBdVWkKu0d69eyXfTEdHOwwZ0l9KsaWtyANV5CY5zbryQAVhOpabjYWWUDynaTamu0DnziNgb78SNjbZuZwfP/4JXbsG48svZ2PBgl+wYsU2pKVlonLlsvj00w8k850cxHCdDh06hqNH0+Dp2Vg6BxYsGAZ391/g6VkbAQH0/bRHVtZraNx4A8zN7RAWNgxz546SgmyYaqwgSmEAgNrmRTAtLBzCDM96pqDzyY8ctWlymalm2rRpEg3Mi1yKAeCLvLrPYG5FEQAKnz2Kl4CGB4gSrYgIJlCr/VISRatkqeXBFrxc5dGU+TVl6+tf7mNoqshieX+Cw5AXyYYNW7B+PXPKOqJEiViMHNkRrVq1lByoN6xZI13S/QcPlvLuyp3W2cbq1dsQFJQGKytr+PpaSkBHLXk1wf2QIa8hMNAd1tZdkJZ2Hi4uh7Fp07IcImQxdo5FzqHHdRb0GwLA6DNBymVAGTOqd8OGIFSq1C0HXIeEEAAmoVmz7By12grHMWbMRBw6FI6srO4wM/NHqVKBmD9/Js6csUX58s1zHrtzZzcGD66kKhvCDz/8jAULNiEzcwjMzGJgable0rQ1b/5fu8IdQTNQRcm+Zh25CVLIlDLRZTbmx8LQoR/DzW07nibMQHz8EZQp8yNGjeqJTz/diZIl58HKyguRkX/B23sbtmz5S5Kr0EYSNC9YsA729r3h4EBeRnP8+OMIJCe/hqpVuyEk5D4ePLgNK6tv0ajResTFnYel5Xxs3vy73uASpXPWVY/vslKXgfz2peZ5zXHJtYRCU0jZPmuiavZNShf5R/zFixfx448/SnyhL3IpBoAv8uo+g7kVdQDIg8MQRYsAjNQW8mLXdMY3tEyGomgNPS9MahyrLrOrsaZsQ33zkmTbHANBckFQQGiSWEdERODBgwfw8vKSONwE75wwC3LMAvwJ2WzZsgvnz7ugQoV20m8hISdRo0Yohg/vY2iKeX6n6XDFilU4fjwA1ar5olWrZggOjkFiYipq1iyDJk3qS9pFzaJPqyUAIfeaXOPMvwuTNdv755/DiIurBnf38pIJOCHhNIYNa5oHfMr7PnfuHAYN+gCOjnthbu4gtR8TMwEDBlihdOkR8PWtl1P97t0j6N27FCpXrqxILlybBg06ID19HKysvFGiRFUkJ59Bw4Z7sG7dUqkNoYFhv2rfDX2D0AewCTQ7d34ZmZlfwMGhuQTqHj+ejuHDbXHtWggCA/vC2bmH1DzbiYzsg+XLZ6BOnTo5XbKN+fNXwN19CGxtHaTsLCdO/IX9+7fAy2uClFIuJGQBHBwS4e7eBJaW1/DZZxMlp35dheB1//6DOHIkEDY2VujcuZEUjapGK6wZ0apooZ5BJSXjEu+AANmCR1KuJVT6UaR0SpQ511IOABmlS+qWpUuz9+iLWooB4Iu6ss9oXkUdABqiaBFRmPnJ3yv3c9OXbUDbkgtzM5/TZ3YlSGNRY8rWt8WEXw3756VO8FtQmUwEibXgMNQEuuJS4X/lWj9y+125cg0lSthg795AeHi8Ajs7F2laqalMkfYHZsx4JV+glUEhq1f7w8mpMayt7fDo0WXUrZuCXr0ME7rKtVqU3/79JxAQEC5lqahRw12K1qUWl3tMaHRp2jx69CLu3o2Bi4sNWrasAT8/P72nwaZNmzBlynbY2/+VUy86+ne0bXsCtWt3g5tbG9jbuyA6OhT/x95VQEd1dOEvG3chSggkeKFY0SDFgluQ0uDu7hZIAoQCwT14cYIESinW4u76Q4pFiBP3bLL7nzvppI9lN7sboUjmHA7tMm/ezJ15M99c+W5a2jUMGtQqV+NNfSRH+aioKKZVlSWm3r37ECZP/g1GRhsglVIKxdvQ0RHD3Hwtbt8+w8ztNF90MSD/U3WAjrpbnBBgk8xOnToND4+1kEiIBzEKTk5SbN++Ch4ey3D9eiOUKNGTaQclknTExnbC/v2LmZ8eT2VHa/zUqfO4e1eXZT0BKPr3CkxMniAg4B3E4my0aUPg25pZCihXNFH4CPMby47hwAF/HD8eDQuLlsjOzkBS0imMHdsIzs4NVBoujVE2olWlB4u4Un77xb8BISDk37E6pN95DY/2ZlqHQj/uP/74A9evX8fq1auLWDL/bfPFAPC/lf8X//ZvEQDSpPG8loooWmiT4qnFlKUzU7YIOABUlKtX3vMcgPGNTZk/W358GeW9V1bbSYCSDsyiIMzm7ycASJpNniqMgCbPFqFI63fz5i34+T2Ahsb3EInECAg4jurVR8LRsRprNjExAllZJzFtWr8CEcEeOXIWISEVYWOTE2CSlSVGWNgRjBrVRq3sIFeu3MTFixlwcCCzqQbevr2FqlXj0KbNjww0CUmVSd4EcBRpSmiOyARKfp8U9RoREYGWLd2gr+8PHZ2yDPAkJfXG/PntUavWD7h06W+kpYlgaipFq1Y1c82/BGqGDp2Amzf/hqamPTQ0XsPHZw46d+7Exkr/vmbNcWzfHojk5A7Q06uL7OwgpKW5M7qTRYsoGjYHvP4XkcckB8oIc+PGDQZo69Spw/pB/z916gZoaU2Eri5xMO5DnTrvsXXrSqYpJHlzQEJjvHbtHh49CmPrpE6d0mjePAeY86IIxFB9oWaL1u+4cT4oUWIK9PQs2OPv3z9FiRKnsGDBBGXbBPv3/AItlRovQCXeL1WzpuT1KiGQ5/NA9YWE3+poCTnHJO0hvJDply6IRL/yNZdiAPg1z+4nGNu3DgDlRehyXztuqiuMPMHycvWqAsBUNamp48uoaFnRZkzaBxo/bfRCc29RAUAOjqlPQhobfujKav1ovshEu3q1H/T1u8HMzJ4N5/59P0RHP0GtWj2ZL1dq6gN061YB9erlHTyh7BM7cOA0oqOrMpMsFYkkGyEhRzB8eHNYWOQc8vIKyZJMZpQ5gwDHunWHEBdXHe/epSE5OQ2WlrqwsHiImTP7sINP6EtI7QkBofAwpHkYNmwSHjwIgkhkAkPDeGzatAS3bt3FypXk41abEVfXr++IHTvWMa0cN+NTX4RAbfPmLVi8+BpMTHZAJNJHWtoNSKWjcePGH8x3ktbCunW/IyWlBnbuPAyJhDKvRMLc/BKOHVvHcp1S++pqtZXJXNm/07e0fv0WXLx4HyVLWmL06L65/ojcbHz27Fls3nwIMTFxaNr0Bzg718OLF+8hFktRo0Yp1KtXna1v+kP955cPLh8O7jgoI/9MmgfSIHIAKdRq0X8TIJ86dT0cHLygpZWjDU1MfAstrb1Yvny6smF9AAALA2ip9EIVK8mLtFXxUaXVVAXYioiqaX3TRUQIALdt28bW/fTpqsldaSc/0wrFAPAznZgvpVvfKgCkzYEfFrRx04FH/88pTuhgow2lsExasrl65a2PgpibVfFlVLQmadykESUQSWBXXvRmYWZM4f3g4JsOFzrwSMspPAyonpDZn9LdEbky1du06TzKlh2VOz+JieGIjd2J2rWrIjtbglq1yueZDo3e/euve3D4MPnNidCzZyv065cDxoSFsoccPRqEkiWbMBNwaOhjlCkTCjc3CrbQYFVJbnwNkfzOnTsHb++NCA+PgqOjPTw9J+LSpae4ds0BJib1oKOjh/j4lzAx8ce2be4fgDI+fiEgpN+ojwROli1bhR07ImFmthoaGjpISvJDiRKbcOXKCabxoNynlAWESHCVATM3txG4d68TjI175A45ObkTtmwZi6ZNm7Lfjh8/h2fPTGFhUQkvXvwP8fEPMWDAd2jUKEdLVljfh6r7Ja0VCtJ5/NgeRkY/ISPjDTQ0NmH37sXMRCtbSHanT5+Bn98jVKzoCiMjK4SFPUC1aklwdW3NZJpXKrt3797Bw2MVgoKIN1SKypWN4e09/aP8wlyrtWyZLx4/doS9fRtIJJkICfFD9+7G6N69C5sPZXNSmJo2VWWqSj0OAFUJllOlPWV1uOZfSPpNz8hLZ0d7OdUXUmCtWrWKzdGoUaOUveqL/vdiAPhFT99/3/lvHQDyCF3a2Aj8KfO1y++M8Vy9ivKgEgDjGQDyos9Q9P78AkBViKzpnYUJAIVAm8bKff5INrSRy9P6UVTf4cPXkZFhBg2NJBDgK1NmGKyscjJrvH17GQ0bJsLVta1KU7Ry5Xrs2PE/GBmNIL0ekpM3YvTouhg5cugHz1Nfbt++jxs3XiMjgyhFzOHi4pxr/r1z5z7zP8zI0IWJSRaqVbPCpElLoKExD4aGzkhK+hNaWj5wdW2D69ctYWvbFpqa2khOfogSJe7D23voR2BCdgAkEw4I27btjcjIGTAwaJKb9zgxsTFOnNigcpo63v6MGfPg52cCc3N39lN2diKSkprj1KntqFSpEvuN1uSFCzfx9GkE1UDt2g5o2rSRwqwgKgm/AJVoHfTp4wkLi9+goZFjpn3/fiM6dw7G4sWeH7RMcluyZCU2b/4NUilRBkXCxaUfatd2RWTkIYwc2ZZd8nj0tjBym7sgTJo0D3fvVoG9fV+2NsPCfOHiEodZsybkmuiFoI6ofLy917OsIcbGemjTpjr69OnKNI3CCFlu7uT+iLzjRalpK4DYcwN9PhUAlAfkucaV/80vRrwu90GlS8nChQtRo0YN9O5dsLR8BZHZp3i2GAB+Cil/xe/4VgEgaYD4wUrghjYNOgyU+drldykoMqEKARhpwfJrblYWzCJvQxX6OBIQy0ubU1g8g/KCWkg2JHeufXv8+DGOHbuC6OhEVK9eCk2b1sf27edgaOgKc3Mnlobr6dN1MDDIhJERaX3SYWubiEGDOudpluUyIDDVpEk3aGuvg75+TjRsSsoTiEQzcenSEblykOdnRNqhrVuvwcamHQs+iY8PxYULs/HsWXlYW89n7dIhFRs7Hi4uUujrt0Zyck5atFKlSkMieYFRo5oxDkNVS58+I3DrVnOYmQ1gbYvF4UhNbY+zZ/ezSGEhSbUy7RyZNV1dByE1tRNEorKQSo+gdWs7bNq0Mrc7XDtM809rRNk6UXUc+a137do1jBixAebmh3LnKTb2ABo3vo5Nm3w+aPbChQuYOHEbsrIWQiIpA4nkLcTicRgzZh3S029g4sQuTPPMtXeyZnj6prp3Hw8rqx3Q0TFlbaelhSAubgz69GmH58/foXLlUujcuR3Iv/fIkWNYteoQkpN1YW3tBDs7C7RrVwr9+5Nbgkbue4TkytSmUKtF9Uij/F8BLUXzIo9qJb9zWFjPcSDII+tpHr29vfH27VsmP8rVO2HChHyT49M+ROndSKtOxNLEhUk5fpUVDw8PbN26lV2aKbBq/XrKW56TL7qwSzEALGyJfmPtfasAkAd/0GZLG4eJiUmROrLLatDonRyAFYa5WZ18w/nxcSQAQBoSoZ+NOp+KrNaP06jQ7+QrR32i9inqdunSE9DT6wgdnRJ4//4GbGweQyT6HmXL/nubDwm5ixo1XqF69YoMNFP2B1mKGn6wywYokKycnTvD2HgvdHVL/gMAHyM2dhhmzZoKR0dL/PBDDaUR1deu3cC5czpwcqqXK4ojRybi2TND2NgsRmammGWXSEoajxEjHBEf7wgbm6bQ1TVGePgT2NsHYcCALmqZUSmycdCgmcjKGgBNTUtIJHvx889V4eU1h2lp8iJVlmd+JMLcnTv34d27KLi4NGCp7oRkv/z7UNUXVZ01Ia8uzRllb7h+/RYsLUugc+eOH2RBIY1ky5Y9kJw8CmZm3ZCZGYKkpIlYsqQPOnXKCV7hZcmS5dizpwQMDXshNpbSmJkiJWU2GjQwQK9eddG5cyu53eVmeAKA3bqNhobGHBgaVkJ2dgpevPBERMQZ6OhYw8amAUxMLOHo+Art2jlj9eq7iIvrBENDJ6SlHUWFCiYoUSIWHh7d5fIu8vcIASH9RoXWNA8wUWY2LqjMVXleHtWKKs99ijq0j5KMSGb3798HAf+TJ4kT9A3bWwgINmzYEGPHjlUrH++LFy9AFw4KtCIqH3LtUAYAfXx8sG7dOkZATXuSl5cXdu3ahb///rvAKQLlybIYAH6KFfYVv+NbBYDkSM4pTmiTUCdCNz/LQahBo/fSQUaFNBCFEUGpCgAsCOgsCNE09U3WvC709eOHHh0y27btx7VrZVCqVNN/gJEUAQHzYW5ugOrVZ0AkyvHRe/PmHDp2BFxcmsudDtq4V6/ejXfvohmX39SpQ1GlSpXcutOne+L0aS1YWU2FRJKC168noWzZGmjXjvIAv0WZMlEYPJi0hP9Gg8q+6N69+/jtt2TGPcjL/fu+OHlyO1JTR0NTsx7E4kswM9uJy5f9ERISiitXApCamoUKFUqgVStnmJmZqb2ciPdv586DiItLQseOTRloE/ouCoGFMlJleVpCOlBv3bqDx4+DYWVlhB9/rIuSJXOAclEXH59V2L79IqTSthCJgmFm9ggHD25iASf/yvg+pk1bhLCwRGhrZ6Nfv06YMmX8B/51JAMKclm7Ngy2totYQEtiYjKSkoZi4sTmGD16qEqaoYMHD2Pt2kvQ1u6K0NBjiI7OgKZmLxgZVURm5haULl0W2tqBMDB4ipSU0UhMpItDTrBMWto81KxZHePHV0e1ajnR6dwPUFFAA+0NPK0jB4afmlhZ3hzLo1op6rWgavt0SaH1L7wADhgwgGUCoUAtujRRZPikSZNU5r+UfTfNmyoawLJly2Ly5MkMbFKhOaRvZ8WKFejTp4+qQ1K5XjEAVFlUxRXlSeBbBYB0yFGhzVWVAI2Crh6uQSOzhbqZRFR5t7J8w8IsIvkBnfkhmpZHZUNj4QBFnq/funU78OBBVZQu/WOuySww0AcVK0oQFVUeZmZVkZ4eDWPjZ5g06We5Jt9Xr15h4MB50NQcC2PjGoiLu8hShvn5rc8FXDTnHh5LcenSfSQnv4emZik4O3dBvXo9oa9vijdvjqFfv4q5vnDy5oCyk6xceQApKSVRtmw1aGuLYGX1Gg8e/A937gQiISEctrblUb16NUyZ0pb56HEtnTwiaVXmWZU65IdGhx6BSwrmIK0tN5dxQKgoawnNyZ49R3H/vjasrWsiIyMBmpr3MXx4W0bMXZSFKF1atuwDY2M/6OjkRHdHR3vDzU0MT89ZH7ya+h8SEsLGKAuieTDV+/fvMXjwVMTENIaubg2kp/+JKlWisWfPBpVdLUgeFFG8e/dRRu5sZrYCMTH2MDAoB7E4AMBi2NtTlhZ/GBh4ISTEDCYmlSCVJiIxcRIaNCiFpUtH5AaZUXv8DweEHAxy+h9hVguuxRZGHNNz+aVMye/8fe4AkKdO5OPr3r07M72qSniuTC6qAECy8tBaJLBJQVi8tGnThl0Ali1bpuw1av97MQBUW2TFDwgl8K0CQH4QkizyCtAorNVCmwPndiusPLrCvinKN0wHCN2Q88oiosoY1QWAnHpEmDlFqPUTakDocONBLAEBAVi9+iqsrHozTUpo6FVYW9+Au/tI3L//AM+evYOlpQHq1KnOTIM8ipMfiNTuli3bsXVrFuztx+UOLTx8Ojw8GqFt23+DRKg/69ZtwSaf1dBPTISuSAOxeoZwG3EQmZmx+OknM7mRpdQoRQfv2XMfEkllhIe/R0zMXbRqVQINGtTCiRORKFu2e+67KXK4Vq1QdOok3+SoivxVrXP0qD9mzyYfvnqQSiNQsmQS9u/3/Qi8yfq90dqk3ygLi6/vJTg6EoG27j9ZVW7B2Tke7dq1VLUb+ap3+/ZtDB68CubmR3KfT0g4jSpVDuLAgU1K26T+c2JqAtj0h0Dl3r1+ePEiBPXrV4Gb20/M3YPq0kWBfLsory9FjMor5E82ffpihIVp4eXLR9DX90JWliX09KpDIgmGROIFJyddtGlTDhcuaCIlpSliYyVITT0LG5v7mDt3MFq2bJZL60NAggNALnMhIORAj0C7PNOv8BviEbL0mywnoSINo1IhKqggzwc2v20V9nNkXSBNvVBbT6DL399fbgadQYMG4ddff831y5TtT7NmzXD+/PkPflYFAJJPMOUgf/78+QcXRzc3N7bmNm/eXNhDpzHkUBF8o+WbHnxhzHkxAESRkhwLtWAEVjgvXGHMnbANHs0s5KbjWkHaIzi5cn7fqyrRNB8vN69zs0xeWj9uwqFDjwDIL7+swaVLL6CjI0Lt2uUwdmz/D8y3NAZqT1Ee3l279mDHDg2UKkVmmJwtggDgnDn1ERQYiHP+/jC3tESnn3/G6sWb4RwUgPESQ2hp6mF3RiT2WzmgvdtIjB/fXiEwWL/+AJKSGsHCwhHZ2VmIjQ2DVPoHBgxoi02brsLRsXeuuTow8ApatsxGs2aN8yt+lZ4jM33Dhh0hla6FgUHdfwJQZqBvXz14es5W2Aa/JNCcEan0tm334eCQQw1DaycqKvsdAWMAACAASURBVACVK7+Gm1tHpTQmKnVUQSVyy2jWjHwQfaGvXxVSqQTR0dMwYoQNJk/Om0iZ1h3nwlRGTE3j9PBYjHPn/scIsEWit5g5cxDzNxQWksuYMbPx9Gkd2Nm54eXL1QgODoShYQuIxRSZfRTW1m8wc+Zw9Ojhip07D+DMmXtISkrB99/bYuzYwcz3T+ibyc25wmAdDgC5nx3VITAj1JBzMKgIFHIwyM3GNA4eXCK8HOV3fj53AEj7jNCVhnz+bt26JdfvjvYybgGSJw+SPe3TwqIKACzWAOZ3deX/uWIAmH/ZsSe/VQDIN2WSQWFSnPDp4NoInj+Yb9yFlapNdto5ACQ+Qyqcz5AOw8KIbFYFAAq1fjyDiDKtHx16FARDmy5tyjNm+CAsrCb09CrgyZMryMr6E/Xrf49WraqhR4+OCk13XKNFciCH6+HDFzITsIlJdcTHX4aBwX442mki+Nw5jMzORhiA5QB0NQxx0MAUZSQiZGSIkZyVjnZIwOYjh9GsWQ4XnryycOE26Ol1xrt3jxETEwgbmwrQ03sBd/f+OHLkDJ480YOZWSWkpr6Hvv4zjBjRSaUIZdl3kbvAmjUb4Od3moEv4h8cO3akXDlQSreePefAzOzP3GaSky+idOk1OHVqn9xxcHMpgQ5aKzQfq1bth1jcGNbWFZCWloygoN/x88+OTKshzFpC6eMOHjyCwMAINGlSC66urnJzJKuyRfH58/M7DG/vrUyDCYTC0TETu3at+yhFnbBNnkuZBykpU4r8/vvv8PQ8B2vrZdDSMkJKynOkpk7D8eMbP4jIpjXfps0Q2NjshJaWMbKyUvHy5SpGIVO9eiU0aFARI0cO/sA/Uhg0Iztu4RqVDdahPtNvPKWe8LtRZjaW9x5ZkmquJZRNv6bK3FAdeVx7qj5b1PXIOkF7CPeDpbFS0AZp4gorgEYVAEjjlOcDaGdnh5UrVxb7ABbBQigGgAUUajEAzAGAfOMtoDjZ45zahf7mGTUKI1NHXn2jDZ9M2UXFZ5hX/wuq9eObNJkAfXzuwMlpAm7ffoLUVBtkZp5FzZqUGi4BTZpko0qVysyXTvaGLisbiiRdvXoP3r2LROnSlmjbtiE8p03DI21t2IpErPri9HSslehhuYERWuubMj+5R6nvMdlEB5efPFEIZuiAOXjwOH75ZS/i4ij9VHVkZ19B3bom8PffxQ7LBw8eISAgHNbWlKIsx1ydn7JgwVLs2hUAA4PJTCOUluaDYcNqYvr0SR81R3mEGzd2hZ7eQZYSjkps7DJ06hSGFSsWfVCf2uLUQdxcyoET+dYdPnwJ0dFEPp2Bxo2d4OKSk7KOX5zI3OXmNgoJCY0hEn3HtJ9Nm5rA13eV2ocuHdQnTtxAREQCKlcuiRo1HBmVB2mzyYdRkb8kjYEuDSRv0h6rSkzt7v4Lzp2rBhubf830kZGTsHhxGzRv/m9AD421a9cRyMqaBhOTnCCO2NhrsLTchb1716gVvS1v7jnI498Wp4rh0b9cSyg0G3OeTP43b5dni1GkIRSCT06yLNRGCoNT5PX1cwaApPmm+RcCwHr16jEAqOwyoOybpG+EZEeXI4rsJfMw54uU9yz5+VEUMEUhExicP38+9uzZA3JtEeYqVvZeVf+92ASsqqSK68mVQDEABMviUBCKEy5YfiDRhi6bPzi/RM2qLluuAaT6haX1E75bUf8LqvUTbtCXL1/GunVvYG7+E27degULi7qIizuKKlXEePUqBUlJx+HgUBMGBmGYO3cIo3fIq9DB4OW1DFeuPEdaWhbC3t7BY4NMOP2T7eMPsRgj9AxhnJ6JQRBBDxLs1xbhp5nTMWrcv/6DwndwU+ORI0fg7n4K2tprWUYObe0kaGiMwZ4985T2S9U5JY1QrVotoaW1G7q6FdljaWlEiTMK9+//KfdwW716Pdav/x0SCZlrw2BkdAMHD/p+4AzPATutVzo45UWh07sJUNJakge2KSPJli1JKFFi4T/BOimIj++IHTvmMQJeoYkzLy0MZXYZP34dHjwwR3q6DoyNU9GqVRaWLZuepzZRqLkUHv7k80cR4PRbkyZN5OZr3rRpK7ZtS4Wd3TQm0+zsdERFDcD27dNyo3X5HPn6boGPzx8Qi9vCyEgbZmZXMGdOT1StWoX5VRYkmIePgeTDff7kuTZQX2RTA1K9nL7n+G7SnFLhv3OCafq+lJmNubaQnpeXaYN+50BImG1D1XVc1PXoOxeS55MMCgMABgUFwcnJ6aPvjHj+5s2bx4b1/fffo2/fvowvkBdPT0/4+vqyc4XyUxfzABbdCijWABZQtt8qAKRNj0xHVGgDoU2yIDc0Zdx6RQUAabPjvn7030XFZyjb/8LS+gmXL/mf9ew5A1FRHRAfbwFDQ8DI6DxKl26Kp0+j4OCgg4oV+yEm5jZ0dXdg1y6fj7j/qD0iOL558yZu3bqPmzfNULIkRZDq4sold9SU7sR1o2ykamigd1YWKg8bhlp16uDYvn3IzMhA0/btQU7bdNDRoSvM1MADDOj39et9sXOnGUqUGMIOXW1tHbx/Pxdz5lTAzz//XMCvMudxel+NGs2hr+8PHZ3S7LeMjL8hFvfF48cXFR7sxFd26NBR2NnZYOTIEbkmSr5W6ILCNd751ZCMHTsTf/1VB+bmfXPHGh8/AMuXu6Jly5a5mkJaJ1yjxUGMUKYbN26Bl9dr6OuPh7a2PdLT/wepdBGOHBnHDnHZIhyDrOby0qVLmDZtObKy6GKQDAuLYGzZsoQd4sJCIHHAgCmIjW0IwBqZmRfRrJkpVq9e9MFhT6n1Ro5cgPfvyyMlJRqZmW9RtizlnLVHWpo2TE2lmDjRDY0bN1Jrvrl7CH1TsmOQN15OWM+1r9ycKwSFJFOu2ePaQQ4GqU0eFJIXIOTPywaXECikf6NnC8OdRC1hKalMYyQTsBAA0nfz448/4tmzZ4X5qs+yrWIN4Gc5LV9Op4oBIGWBSGETlh//vByzXE6UbV6Ezupm6lBlBdGmTH0n8EkbIP13UfEZ8v6TNojz+tEBlB9fP0Wg4/Tpc1i37iaiorIQFRWP1NRoODgYIzu7NlJTw1Cz5oB/KDakCAkZh02bPiZ2/XXnTvguXIiGGho4FCNCls5C/FC3JwNoMTERePywKRz1o5AqlaJ63brYvn8/074cO3Ya164FIStLGxYWYvTq1RLku0OFpwUjeVNdAk/Hjh3D3LlnYGW1GRoaWowkODa2N7ZunSoXuKgyn/LqTJ48GydOiGFiQunaJEhI8ELPnuZYtMhDbpPEVTZlyiJkZZVEdnY0GjasiHXrlrC1yU2N3Fya3z7RcwcPHsS8eadhZrYVmpqGDLilpw/CX38dyJUb1RP6onG/N256pPUzatREnDxZA+bmE//RJMYjMXEcpkypgBkzZnzQxbwCPegS1L59P0YKbWaW47sZHr4BLi6BWL58wUdDJT/RMWNmISDgPQwMSFtYFd7eMz7wAVyzxhdHj5rAwWEAez4u7gHu3JmOunVXwNr6eyQkPEFaGvEWeqpMkUP7BfkWEshSFqyiaH5kASEHZ7KAkPsOcmAnBIQcRHLTr7x3CQEhvyxzACmbvaQga6kgz3IASPsQ31dIc92rVy9Gx/K1l2IA+LXPcBGPrxgA5gRM0EaiLgAURtkq49ZThahZ1anmGgROf8Bvv0XJZ0gAkDPu00HOfRu5+YmboIT0E9w5nw4LRbQWwjHPm7cOSUkdYWbmhOTkKERGxuPvv5fA0jITiYlNULHiGFadeABjY2dg795fGL8aLxRB3KlRI+zV0UFVXV24habiYto4mDt1gVPZssjIiERc3FB4eAxDqVKlWGQx9ffWrdvYtestNDRq482bWMTFvYSDw11s3OjOfCpp3PxwofHSeGg+x4yZiadPCeTUhEh0HS1bOmDFCm+1feDymndKkzdnzkKcO3eN9aFdu6ZYsGC23FRhZHL68UdXZGd7w8ioGSSSVMTGjsKUKbXRq5cb67cq86DKOqT1MHbsdFy58jdEItJOvsCcOSPRp0+vPB8X+qLROpo/fxF27oyFgcF0iMUJSEt7CInkD/z4YzkMHNgErq7t2LipLq13GgP5+tHaEn6vZEpu23YY7OzOQkMjx8czNfU5tLRm4fz5gx/1acmSVTh8OAN2dpMZgI+I2IRGjSKwYoVXbt25c5fi5s26sLVtw34LDKSMDsFwdh6VG5QSHLwc06ZVQuvWrZWKTTiGwpoHeimXqWwqO6EZnsChrNmYA0RVzMb8G6B2hBHH9H6hzyL3RVQqjEKqwC0RQgBImlsiYyZN+NdeigHg1z7DRTy+bxUAchBF4lU3QINrIugQVNXfThlRs6rTLC/AhD9bVHyGXGtB4yXtFwecQvAnj9dPGOGriqlx8WJfvHvXBDY2NdiQMjKSEBm5AmPGtIeHxxakpzeFSGQOsfgCevWqjFatmjHzPWWJIM0kC/wYMgQnjIzY82dTUjAy3ByJhoNRoVItJCf7oWfPUpg2bfwH4t68+SBu37bHy5eGMDKqDC0tPYSELICrqzEmTBicG2DAD1saFzf5k9mRgiYITJLZiUxkBYk8JJlSDtKbN59DU1MEZ+eqLI8ogTtO56NorZDZe/DglTAzO/5PFSni439DlSp+2Ldvo8pBEqquRd5XMqmS3x/XmKr6PNX73//+h27dRiAxseY/1BzlYWpqgH79+uD9ez9MmNCccasR4CYt5v37D/H773eRlJSJcuUs0LNna/ZeWputWvWGRDIfRkY5vqFRUXtQv/4dbNy49KMutWzZC9raPozQmYpYHI/37/vizz935QLL06dPw9v7Cuzt3aGtbYoXL5bg3bswtG5N0cM5NC0hIfPg6dkCjRopNgMLTb40BvqGVPke1JGjsC7/LoWk39wUL9QS8qATDiBlASG1yQEdyVc22wavLwSE9JswuIQ/X1TjpXfL5k4mgnYKxjh69Gh+RfjFPFcMAL+Yqfo8O1oMAEmjlM40CsoiS2kGedADbWzqEDorImpWdVXQxkqbMGlBZANMihIACs3M9B7SuBWm1k84fsrjuWbNZRgbt4G2tgGioy+hdWt99Ov3Eyi7xdmzFxAVFYekpBhcvvwcyckWKFu2EsqW1cLAgS1Y33o0bYo/DA1R8p8Ubn0iI/GsZBVUqFQF7dpRrtvuH1Go+PmdwNatSRCJ2sPIyJoFBcTEbECFCkn45ZchH5gFZeeLmzhlM2zImuNUPQCvXr2OfftewMjImfHgpaXdwMCBtVCnTm2lS4Vyn7ZrNxRmZqegqWnCzK8xMavRpUs4li372AyqtMFPVOHKlStwd1+MgIDKLK1es2YNWR7gV6/OwNUVLKsCyY80O5s2XYelZRcYG9siLOwBzMxuYdasIf+Y5X/D/Pk7kZXlApEoCQYGN7B580LmqC9bunQZiuTkiTA1zZFrWlog0tMn4cyZXbl+pTSnK1duxB9/PABgBl3daEYVlJ3dEubm9ZCc/AhlyjzGhg0LFaaVy8ts/YnEy16jaJ0KtYTc1YF/38K/aX+kiw3PUUxtKgsuKSpOQqHc6B3CzCn0b5QFZ9++fYzs+WsvxQDwa5/hIh5fMQDMiXCjPxRAoajI+tupe4uXR9Ss6tRyrR/1gUCnovy0ZC7kPmqqtq2onqyZmcZL4JN8DIU0EhzY0G+0Eaur9RO+n9754MEDnDlzB6mpmWjUqBJatGiWeyDTTX/OnPk4c+YNxOIWMDBwRWbmE9SpYwBb27vw8hqGVUuX4tLu3eiYnY03IhEemZtjp79/bi5Ziuwj53AyAVN6JpLt0iVLsGO1LyQScxja/Qxjk3JwdNSEpWUU5s7tqZAMWth3GjdlDyC6B0dHR6YNpAOV2hf6vHHfKXmAkGQ4b95GaGn1gIlJTiq02Ng30Nc/jTlzhqs0pePHz8Dp0xEQibpBIglhASQHDqxj1Dmfc3n69Ck2bXqEsmX7MWAhFmfi5csdGD26NqpXp6wbEka9c+NGSZQsWZcNheoFBf2KiRMbomLFikzONLeXL19h30Hr1q0U5jA+dOgIfHz+hIHBAIhEOkhK2oNBg6qwHMGyhTSc5FcWGRmFlSv34fXrBGRmxsHFpSoWLJij8IIgNPmS1lzVS8CnmCdZszGPJpYHCHneciGpuypmYxoHB5EcDPL3CM3GefkhKpMFJ88WugNQ6r4LFy4wOpavvRQDwK99hot4fN8qACSxEuijkpd/Hte8Efgg4CWMNlNnaoREzaoeBPRuAlWcVkbZIVJYhNZCsMt9/UgDQOZW7mtTEF8/deTG65L2Z9asFbh8OQFZWSbQ0MiEg4MXxGJNmJg8RfXqQRgzpj6TFWUACAsOhoOjI9p36MCasLa2xq7t23Fg7VpUFYnwKjsb37u4wLRECVzftg3jaXypmVgGEXSq9UDVGp1Qo0YChg/P26eN2qa5nTRiBMKvXEHD7GzcIe3wDz9g3c6dDLxyExnXEhKHHpnrKUm8vb09izYmedI6nDFjPaytR0FXN8eMnZoag4SEHfDxmahUbDRv1K6//zFcufIQ9vaW6N//5zzzGStt9BNVoPW1fbsfHj/WhIaGAzIzX6N2bU0MGfJzLk2Nv/8fuHDBHI6OTf7xe8vCmzebMWZMQ2Ym5uBaGG2sqPv0bf3++0kcPvwXxOIsdOnSBN27d5NLiUNtUIT6kCFe0NObADOzakhJCUJc3FKsWDGMmeiFhe8ZtL98CpNvYUyREKjxdcr9egmg8UwbspyE8iKOVeUk5FpJHsQizG8sjBTPa3zychST6ffFixf45ZdfCkM0n3UbxQDws56ez79z3zIApAOXNj5F/nlCzRuBL34Dzs+s8sOZzJSqAEAhrQwFIsjjapPtR0EBoKzWT+jrR/0hAEh1hAS1QoJYVYl48yO/GTMW4eHD6nj50hZSqQOSkv6EkVEEo2HR1r6G779/BDMzLSQllQKgBwODQFSsqI/9mzYhMTYWekZGyExNxUkbGzjq6iI5OxsDY2PxIDkZ/traqEaBBZliXElJw2BNTSxYvQEdOrSUG2wh23/yA1w1fDiOmJrCQCRCpkSCXgkJ6LV8OTp2zEkvRgEqpEm6fv0eHjxIhFRqB6k0BK1alUWbNi2YNovmeM+eY3j0yBaOji2YrAMDz+LHH9Pw889d8hQbv8TQHJD2S5U1lp95KIpnSHNNUbk8aCghIRVOTqWY6Vao7SY3gBUrTkBX14WZ6iMjH8DJ6S0mThyYGyjCqVK4z5ssH2F+5UJapWXLXqJ06X8JuIOC9qB/fzHzV+SFByXQ3NH3w8mJi0JuRdUmv3jSmuJ7Hr/EyNNmc19ADgY5cBRqCbm5OC+zsTBinMYmBISKgkvkpajbuXMn85mdPVtx+sOikt2nbrcYAH5qiX9l7ysGgDk5ZWnD4BGl6mreVFkS1CZF6ZqZmeUZJED1VKGVkfdOGgMdmPkha5UXXCLr60fvpN9IXrTx0jNUOHghAMO1WarIRNU6dBB17z4BVlbL8fhxEN6/J0qTGGRlLYGJSStUrBiGypWzkZHhgrJlW7JmX768gbPHB2KPcTpaGhjgTFwcRicm4mTFivhOn7J3AOujorAgOhr3zM1R6h+fwZdiMdplZeHx27cqB3Ns27YN4UuXYqEgItknOhpZY8di0qRJOHfuPI4ceYD4eAM8ffoY1aq1RM2aPyMzMxmhodswc2ZHpg0keb5//x57957E27eZ0NCQompVU/Tr58pM7/LAC18vNCfq0LsQZRBpImk92tjYqDoVhV7v1atXWLfuGJKSHJjvpZVVNCZN6s3kQbmBHz58yPpYsyZFWouYZuf06VuIikpCtWql0LZt0w8iwXkH+TrlgFCeKZ7qEnh/9Og57O2tmclYGFUuHCwFGM2ffwkODp658xAUtA6jR1uja1dXVpWbfFVNSVfowiyEBoUAllM8CWUqq83mF0JZoM3dRIQaQh4gwq0HnFJGttvCfUeWk1BoOqbn+T5Ea58XMv2SP/fYsZQL/OsuxQDw657fIh9dMQCUsoOXtBC0+XMgRIKnG7wifzt1J4YDwLx4+tShlZH3ftLQcaoPVfvHzVWywSXCTVg2wpdHTXOyZB4Vy/18OCAUagpV7Y+8etSXIUNmISmpLwwNq+DZs78RFnYTgC/atKmPMWP64ciRa9DUdIOxcUnWBJmBgy6MxlW7ZJhpajJT+rjwcFS0tcVUW1vmUzbx/Xvct7BAnZAQeBsYgHIpTEtNBTp0wGpfX5W7TEEMPkOH4qiJCYzoXRIJ3BIS0H/lSgZcvLwOwNZ2GMLC4vD4cTpEogto1Kg5zM0r4v79XWjaNB7t2rVD+fLlGbigvoWGhrK/S5Qo8ZEfISeoJnlzahR1aEXu33+AnTv/RHp6CWK3Q9OmjnBzc5WrrSLZk/mT3kWgrCARzvIO+gULNiAykvIOV4O2thZCQq6gRo23qFixJGbMIFLncpBIolCjhgXWrVucp59uXhPGfd44IKQ1u3jxapw//x4i0Y8AXsPe/jV8fX+Rm3eY1vyYMfMQHFwNZmYNkJj4P5iY/IWNG+cxH1HuR6xOSjqVF9gnqsg1sPwSqUxbKjQbK9O8CrWEQlDII5HzMhvT8Dmg5ICQm43p36gNHn1P/7148WLmEzpgQA5/49dcigHg1zy7n2Bs3zIApA2PbyzkO0WbCG3ktIkTuFG2Aao7PYp4+mhzpIO8oO9WN6OJvOASWa0fB3/cVE5AKi8+OWG0IR2yhWWKu3r1GhYvPgKJhEh+CbT/iRkzuqNFi5zcrb6+e/H0aUU4ODiz/7969SQirk/FfTtASyMnYZDbu3cINDDAKGNjPJRI8KJkSSzasAHzZ8zAk/v3WZ0f6tfHys2b5YIARfNNY5w+bhxe/vknGkiluKehAasGDbB6yxbGRbZw/j1kp1aCpp4+MrIdoKubhIoVUxEVZYHg4GP47rvyMDNLQYcOZdG1a47PorBw8CLL80Z1CAxy/yxV1iu5Ccye7Qsjoz4wNS0NsTgNgYG/YuzYHz5KYUd1t207hGfPEiCVilC2rC6GD++R77zGsuOib27y5HUoU2YGdHTIbE2k7NGIj9+Ie/duQSKZBxOThpBIxIiMnIlRo8pg9OgR6n52cuuTJnHw4CWwtvaFpqb+P6B7KYYNs2BchvKylkRFRcHP7zgePQpE+fK2cHPrzIKLOI8oXRhprsjcT1pLoVaqUDpdRI3wSyDffwri6iJP88rXqVBLyM3DPCiEm42pLv2bKqns6Bmeoo6emzNnDogChlL0UdDTlClT8ozgz0ucRMVE6d0oKC0yMhJEsN6iRYs8Z8DLywsLFixgigOu7ezUqRP27t1bRDPHwO83nQ3tmx58YayqYgAoYc73XHtG/nZF5bcjj6ePa/1ow1OHVkbe3JNZj/YDZSntZLV+PLhEFa2fOmZGvpkLgQs3xalCk0LPUXQoHajE9UYyunbtDgMKP/7ozG75vBAX3+rVh5GURL/pIj39Ju5c2IxR2dloY2iIixkZ2KanB7eRIxH59i0cK1dG959+ygUz9Dy1T0EZigrJ55i/Pw6QX2F8PBq1aYOxU6aww54OI9IEErAoV64cSxpPc9ujdWvEvjBBHQNXhGZl43aWFozMI1CypC4iIjJRsWIZ1KnTD2JxKsLC1mPhwn55mmQ5aKd55uS+JCdFpjjZsdABuXbtQzg5DRLI7gYaNgxG377dP6i+d+8RnDunjXLlujByZfJHrF49BOPHDyzQ1sNdLOgysXTpr8jK6owSJXLmMjT0DvT1f8fZswGwtj6U+574+PMoX34f9u1bX6B384cvXrwId/eLKFlyEbKzieQ8BLGxt9Cq1RsMGeKGy5dvIiIiHlWqlIazc/3cb1Poi8Y1ZtzkS2t18+bjiIqiLB/Z+PnnxmjTplWhXyQLRQD/NFLUPotCzSvXEuZlNub1ucaQA0VuLpY1G/NAPgKtb9++ZVygfn5+jDaKIv7pW2zYsCHzB6xUqZLKoqPvmHJK16pVC3Xr1mUXOVUA4F9//QXKaf6pSjEA/FSS/krf8y0DQNo8CPgRAKRSVHl0+dIRAkDaeMmspA6ZtLIlqEpGk/xq/eiQI61oQU2AQm0WB4byHMvp3zZu3I1798TQ0HCCVPoSTZqYYfDgXgr7QD5jT548ZbmADx++gPh4I8S9uwoNcSxqN6iLsTNnss1clYAaLmtaG/v378f1U6eQmJ6O8MePsdDICDZaWtienIwEZ2dsEPCN0Xq6d+8ekxVRh/hNnoyqKZp4klEdxjrlcSszGCZVk1GhYjnExVVDtWp9oKmpw1739u0OjB9fk9HTyBbSPpEGmaKZCYQLc7Lyw1KZKZ7kTAfjggXH4eQ0HiKRFnvN69fH0aOHNtq2/TCbxcSJS6GvPxRGRjk+ggRSQ0N9sGbNpHxrt7jZml9UCJBu2vQnxGLi6suCkdEL9O7dBCNGeMHMbD+0ta3YuyMjN6FDh3dYtGiuss+AgWECY6TFIRM6UfLIXoooc8jPP0+HhkZfBAUdRWYm+ZO9gKtrFRgaOiIpqSb09EoiMfE+6tTJxNChvXJ9XjlfHn3DNM80F6Ql6tlzGqKi2kBfvzzs7TVhYHAGHh5dGUn451j+C59F4VoVmo35hUboNiI0G/PLKcmRg8IcuqAcfkKaA16GDBmCqVOnokKFCiwnOAG5oUOHMnqm/BRqX1UNYDEAzI+E8/9MsQYw/7JjT37LAJAAA23idDiQuYsch9UBB+qKnvP00XOkraN3FWakYF4ZTT6l1k8ducjzzaLnKUPE+vUPUa7cSGhqajMzYGDgWsya1SbPmzwdKgMHTkFUVFdYW7eCRJKFwMD16Nw5A9OnT1CojSFAcObMZYSExKJ69dJo2bIpWw9zpkxB8PHj6Jadjb9jY7FPIsFaR0e0NjFBqkQCl7g47DhzBmXKlMGjR4+waNIk2CUlsVzDz7Kz8V1oFGbrirbe4wAAIABJREFU2uBJVgaCsrLwt04KHMaOQb0Gzli+/DKk0lIsq0TJkpWQnX0J3t4DPzCxEgD19FyM48cvQSLRhpOTOTw9JyEsLApv30ajUiU7/Phj44+ilYWmeNkIzh07/HD3rhaMjWsiLS0M5ubPMHPmgI/M3p6e65GQ0AaWljkcgklJocjI+BU+PpPU/k5onmkspPWjw1oIYCk6+tmz/zHNe7Vq3zOfugULfHDoEP3WCRJJGPT1z+LXX31U0uJs2bITW7f+Bam0ATQ0guHklICNGz9MG0jj2bp1B2bP3gRgFjQ1K8DYmCiGVsLWtjyaN88hd87OFiM4eCU8PLoxqhkCTfSdcRMfN1d6eS3BkSOAlZUnI/BOTSUN5mMMHaqDPn1+UueTKPK6wrn4HGhqFLk4cCsBB4XcP5abjelvWlOc/oebjSkP8IoVKwqN+1IdAEgZSGhPpz+kefT29s438FRlIRRrAFWRUnEdhRL4lgEgjyCjjaWo0qgJBU8AkAoHneqSSStbxooA4H+t9VPWb+G/85v+H3+cgp+fNsqUaZXrExQUdAyDBpkx86oi1xcCE25us+Hg8CskkhxtQWrqS+jprcHBg2vkdoVMzF5eW5Gc3ABGRg4ID7/BIotHjeqF3i4u2CcWQycuDvpZWdgtlWKLpib+qlQJNtracImNxdrffmMBHMO6d8fP4eHoZGnJ3jvu4ROcSs3EGpMyqKFjhNcZiZgmDsfSo4cQEhKBpUuvIi2tIXnyQSL5DWPG1MHYsaM+6OPWrduwbNltWFgshY6OBaKjf0Vm5gbUrz8Vurp2ePPmOAwNozFyZHc0b95UYT5r4SGbw5V4Gy9ehMLOzgxNmjRgmkVu3qTLCfk+3bv3AFevRsHSsiu0tIgs+Qr69KmEVq3y9oWSFTLXdtM6pINRlUsW1c0h9L0NKytT/PSTq0oHKc1/t27E1beRafBo3O/ezceoUXYYMuTDoAAyF06degIGBjMhkRDlTjgSEs5BU/MerK17w8WlAaysrPH2rS8mT67HwCdp2em75T7CNDb6rnv1moKgoO9gajqTmcszMuKRlrYW3t7foWvXzp+NGTjne0hl2kxV50Kd77cw6vI9QOg6woE2B4XcjYACVmg+eGAJacoJeJE7hjzN66BBg1iGEB58Ittf2luI0F1YVAWAdGmlSyP5hdI6nDZtGtNAkiZamVtOfuVWDADzK7ni5755DSA3QZAgCjOLhuzS4jduOljpkKVNoqCmVHnLVzalHd8kZYmk+QbLI+k4mBIe1Or6+hX250TatBUrbsDRcQQzVVKwQlDQWkyZ0hJOTk4fUM9wvjDqAx1u3bqNha6uJwwNnVg+3cjIM/j++ytYscJDbjdPnjwNPz8xrKxa49Klm4iKokwP+9GokSbi71/Hr7GxKKOpCY3sbJwTi+EOoKO1NYx1dXG1XDnsPXGC+SkOa9cOJ21toSUSsff88fRvjBPrw0BbH0biVCSJNGHs6IhDRzfD3d0XxsajkJpKYIEyqISgQoW7cHcfzb9Lpmnq1WsMgoMHw8zMhf1O6zQkpD26dVuJ58/PISmpHDIysuDoGAVnZwlmzhypUuS6rOaVa1WISmjFil2IiCgJDQ1rJCZeRenSElSq9B0aNfqeBYqo43cuzIahTqRyftfTjRs3MGXKEdjYrM1t4v3706hT5yJWrvT6oFkCuePHb4e9/QY8efI3EhLMIBb7QVPzDgwMZqFEiTA0auSEtLQ98PIayrSW8pgB6LsePNgTQUHmSE7+Drq6tZGS8gTa2ltx9OhSBq6FPq+KOO3yO2ZVn+MXwbyCuFRt61PXE2q06eLONbB0cSfiZ2dnZwZqx48fz4JB3Nzc5Ppy0/5A+6SiQoBSNiWoqgBQtk3SThLrw4kTJ+DikvP9FnYpBoCFLdFvrL1vWQMoBIAFJVFWtGxo46IDgg5C2ng5UW9RLDNhSjsaGydu5unjhMCP3i8vwrewfP0KOj6S15Yte3HzZjJEIkdIpa/RvLkNywtMRagdEEYa038fPHgEv/56D1pabSGVJkNL6xx8fMYwShZ55eBBf5w+XQLPn2sgPNwcRka1kJq6BXp6N6CVfApTExMwQksLqQBGkxkTwB0dHTRo0QJzfXyYZooOlX6tW2OppiYqGxqy1+z8+yVWi5zQsvtvSEgIRFJSMCpW/BtjxvTGhAlrULr0PGbeppKYGAxgN1asmJ7LJ0frZdy42bh7tyVKlMgZd0xMCCIiuqNZsyl4+TIVZmYDkZDwHNWr6yMryx/TpjVGjRo11BY/XxuHDh3DsWNAmTJdWRvp6TGIi1uH5cvHMhMxv7gQOP3rr4u4e/c1rK2N0bZtE5QtWzb3vdzl4FNnwyDNS9euE2Bh4QtdXSINlyI0dCFGjLDC0KH/Br5QR+kbGTVqJp4+LYm3bwnwakJL6yRsbZ0RHx+DtLTnaN++PPr1I97GGnlmAdqwYSfOnElHerou3r8PglQaiHHjmmPgwP5MOyWrzVIna4nakynzAMmAB6zImt8L2vanfJ5fUOlvHmH9/PlzuLu74/bt2+zyR0EbrVu3ZlpAAoWKeB3V6XdBAeBvv/2GVq1aqfNKlesWA0CVRVVcUZ4EigFgFhNLQUiUFciVBXgIU8jRf6sSpZvflUrvIyBCJhF6F5mpeFaIL0HrJztuOqApGo9HAZNTtzzNKaeD4ME8NNa7d+/ixo2HMDY2QNu2LZj5TpHmigIGFi48ievX7WFo2B/Z2W8hle5FqVIdkJo6AzEBN0FecAQAKxsZwcTAAIa9e2OOh8cHbe7fuxd/rFyJzuTjKZXiuEgEy5o/IjOTnM+1YGWVhAkTerE8xIsXb8Lff3+H0qVbMj/Ft28PoXNnbXTu3I6tG+6bRaasMWN8IBKNhra2NRISKL3cS1SpMhSvXpnB0LAZ0tIeoGnT6oiKOo6RI0uicePGai8hAgh0iO7ceRxZWW5wcKifm8c1MHAlpk1rmqt5pTnYsGEX7tzRh4WFM9LSoiESnYen5wDmC0nzxv3kCtPHVdVB+fpux44dl5Cd7QyRKAhlysRi48ZFcql9SIO0f/9hrFlzABLJd6hceRKMjSsjIuI0dHS2Y+vWpQxEcJMvXRSJkigqKgZVq1ZC7dq12ZqkS97+/f64cuV/0NbWRLt2deHq2uEjLZSiIAhOcCyPfkbVcSvah2guCIB+riZfVcYn1F4KU2LSfkd0L6QZJ5MrfcsU9EF/aG4OHDigSvNy63CKGXrfqVOnmOsJnx95Dxw6dIhFCtNFiYKCqD/kZkCBTsJcxfnukJwHiwFgYUrzG2zrWwaABBzo4KOSHxJlRctF6HMnTCGnKk1LfpchbfT0hw4Tnj7uS9L65WfcdABQtgiSOWn4uK+NkPCXU89wZ3JOpMwBIcno6NHfMHHiOohEzaCnp41SpSj4IA2WlpvRoV09bF60CLVFIqRqayOldGms272b0b8QaCKASnQTr18H4vTp60h8H4QqVcqjm5sbo6p5/fo16x/V4RxrRK68YcMBBAXR+stCrVpWcHPryA4KWdBElCVbtx5CTEwC2rZtADs7exw4cAlPn2bA0PAn1KhRBSVK6OD9+w1YvHgIo8xRp1D/hw6dgjdvKHghFcnJlGFjJgNzycnkF7cBK1dOZKYxAhJErzFnzj7Y289gASzk8xYSchYuLu/Rp0/33EtIUXBpKhoXAbOAgAAWQEIaWfK7oj/0/xQFrOwApgvDrFkbkZHRhJKQMbLuGTN6wsWlZa5JnYLGpk9finfvSkMkKg2p9DY6dXLE6NGDc7vF15o6Lh7cvMnXrOx65dpCdUzv1CHZiGt1+qTO+inqujzNoaz2kqib+vfvjy5dujDOPtnx0d6eXyJ/WuPkaiIrcw8PD8ybN48NmVIV9u3bl72bCvWDfP5on6dLA6074gUUasYLW1bFALCwJfqNtVcMAHMAYGGAM0U+d3xJqULTkp/lJ3wvPc/zDX+JWj91xk9pxDw81iE62gSamoCDgxheXuM/AkDKogz5rX7JkhU4cOBvmJoOglSajfT0nZg7tws6derIctUS2KPbPWkCcvyNiHiWDMKlERV1GnZ27VG1aickJ7+GpeUjeHuPZyBRUaGDn9KxUSEKIjrgVAVNlDKONFc3boRBS8sOWlrR6NOnCVq2zCHGVqcsXLgU+/ZlwsbGC9nZyXj7djYkkvdo2rQjtLX/xoABjdCiRbPcJgloLVhwGo6OU1jwBMmXMrNUqXIPI0f2ZgcxHbxcruoCF3X6TnWJemPBgi3IyCCTbxRat66GuXOnqn34E4/chQuXGYBt1KgBo+MRgoqDBw9jx44EODqOZMBALE5CePhMbNo0mTn+F1ZR5J8pJFJWlnJREWgqrD5+inb4vkZATuiTTL+TZpzSLK5cuRJt2rRRyy/1U/T9U72jGAB+Kkl/pe8pBoA5ALCg4Iw0IwQiaXPiPneySyYvmpb8Li/+XnqewAONgxMTc3b9z9nXLz/jJhmTLGfNWoKXLxvAwaETeTQiOPgAGjcOwZw5E/NsVhhlyH2zuGnu2LHfcOrULejr68LNrbXCw2Xfvn1Ytuwp7OyWICXlGV698oeGRjV07dqazf+bN7sweHAJtGnzIbeesGPCoBvSEh47dgapqenMn45MSaoAJ9LeERgkrV9eYDMvgXTo0B9RUWT6bMCqUU7esLDm8PAYiubNm39Ejk2msalTfZCW1hk2Nj8gPT0eQUFbMWpUNab1IL7CM2fO4M6du0hJEeO77yqgU6f2TBMiDNjJz9zLPkMm3M6dR0Bb2wMmJrWQlZWMiIgZmDevOSgLg6pFFT+5xYvX4+bNmrCz+xdkh4R4w9OzOerUqaPqq9Sux9erUEPI/V5lCdU5aCIAWJipLNXudAEfEBJUC3MS0++U6/fIkSPMvEta9W+5FAPAb3n2C2Hs3zIA5NG5JMb8gjOh9k3ocydvamSjdAsyfRwEUZv8vXRAkC8jaZM+5wjfgoybm7VovgYOnAc7u7XQ1s4JuoiPD0JExBSsWjUdVatWVVkDJHvAqpJZY+pUL1y96gwrq06Ij7+K4OAASKVl0bRpOebjFxj4B7p0SYSb24fZNfjYhVkkcqJRfSCRdIeGhhEkkiMYPbolhg//MGihIHLL69mxY2fg0qXqsLLKMWVmZAQiNXUgLlw4qBBUkkZ0w4bDePs2FRJJIlxd62LAgF4MjPbvPwEBAWmIjyeC9eYwNU2Dnd09rF07l/liCv3duCZLFbArbwzk6zV16jFYW1Pe4HhoaZnh/ftTaNz4DpYsoXht+YXmnDSHu3efQkJCMho2rII+fXqwiF1FZsOTJ//A6tUBcHKaCpFIE6mpYYiLm4+tW93znXIsv3OqiOeRt0caM2Vawvy+u6ifU0RQTRfsMWPGMDcKX19fpWb9ou7n59B+MQD8HGbhC+5DMQDMyQKSH3Am1L7RLVUZv5kwSrcgS0bee+lAo98JAMpGGNLvNL7PJcI3P2MXRpWSqZQO6QEDKGJ2FMzMqoD4vx48OAUdnWOoVq0eypcXY9asUYyGQbaQdoR8vkhW5DdoY5OT6UJYZCM3ZTUu27f/ii1b4mFnNwdicTRevFgKsbgSXF17QFdXgrCwdXB37/BRVg8O3LlZiw6z3r1HIiCgA6yserAupKW9REbGWJw/fyBP/jDStJEJmfpPwHjt2i24ceMpHBysMXZsf9SrV08lUZOT+qBBM5CeTtpKS2ho/Ibhw1tg3LiRCp8n2S1dugG3b4dCV9cMJUtqYMqUvvj999PYujUWkZF3oam5BRoa9sjKegFb2+to2TIAK1cuZH3lmlfu76ZKakB5naEgoR49piAtrTayssTQ0SGSaTGGDbPHhAk5lDryCpkQ3d39YGg4CJqa5oiJOYZGjRKxdOk8hZpXunTMn78SDx+ms3GJRM8xbJgLunTpqJKci7ISN/nyNJYkVyqfA/2MquPmF3J+qRXypJKGfMCAAezPuHHjioRGS9V+fk71igHg5zQbX2BfigFgDgBUB5wJtW9001bVb4tv0vJAiSpLR9F7uc8QN2MKAyDoN34Q0Ib6JWoFFEWVnjv3J1auPAOptBmeP38FkSgAzs6DYWvbAG/e7ESvXgbo2bPbB6Kl9GxTpizAmze60NCwgJYW0UgMRPPm//q4yZsLWY0LAc4xY+YiMrI8gApISzsEIyMdVKjgDA2NOLi61kGPHh8SAPMsEjwSnA667dsPYPHiXSwCtUSJxrC1pVR32ggPb4szZ7YqDOg4deoMduz4CxKJHcuUERLyEGlp7WBi0glpaS+goeGLvXt9VM6G8ObNG/j7n0B8fDJatGiYJ9k2gdd9+/xw+HAaypcfyngaIyKuw9LyDBITY3Hv3g8IDT0Gbe2T//jKBcDePhQODr74/fedH4hXVf9MRRpCAsBt2oxDbGwf6Og0RmbmA2hqrsCxY7/kaZadMmU+Hj36EZaWzRmYkErFiIgYjt27PZkGV1GhtUjBJRQQQgE+hen7p8oeIFtHeDGivYgHGQndHPh+QL99SvoZdcbD9zbZaGX6ndwJiNtv06ZNaNq0qTrNfvV1iwHgVz/FRTvAbxkAcuDH/6YDWRk4o8OPB4yoovUTzl5BAKAirR8BE0W+fqSxIMBHf+gQoD9CTZaQcqJoV1n+WhdqBITZF4StUQTw0aOn4O9/HaamjSASmcDWtgr09Czg4PAXvL0nffDy9eu3sICH0qUnMnCSkPAIUqkPjhxZr1Z+W+obmTspY8nz53+jZs3v0aRJE5b/18rKipkEeWoq4SEtTL3l6/sr/vhDgsjIiggM1ICm5v9gaWkCLS0DlCp1GP7+2+VqOij6cdKkjbC0nAxDQzsEB5/GxYsrUa6cf25KuKioNejVKwmzZk3Nn/DlPCX0L/PwWI/ExJ9gYZGT55bSnwUGzsYPPxhh//4kREcHQCKZDpGoHrKynsPGxh99+mhhzpxpefZHkX+mogCI06dPY+PGOEilzggPj4GxMeVJvo5Jk0ozPjh5hd4xatRsvH7dCdbWTSESaTAqntDQ4dixYwaL/vwSitBPThndDr8cKuLPLGz6GXXkx7lS+cWIB97QfuXj48Py8B48ePA/B9vqjOlT1S0GgJ9K0l/pe751AEigjAMNHkCh6NCgfydNIW22wlymqi4NDh7VcdZXRevHgzyoH8qyeSgzbXKKFFXHVFT1+OFGfyvjLyMfuu7d50FLawR0dOwgFl+CsfE7jBr1A4YM6ftBFwcPno7w8D4wN6+b+/u7d8OwdesEptFRp5D50d19BYKDKa+zGO3b18OECZS5RMRMnFQIuHCALhwHrYX+/WfB3NwdUqkRLl26gbi4SEgkG/Ddd+ZYs8YD1atXl9sd8l1bvz4cTk45PoIhIX/iwoVtKFVqQy7XXVTUDnTq9Abe3or94NQZqyyliI/PJjx8WAP29jma0/T0WMTEeGPx4lGYONEDz54lIDo6jPlFWlhkoXp1QyxfThrTSCYfGpsq6bGUBUBcvnwZvr6hKFt2RO5w3rzZiPHjyzMtpmzh/mVnz57D6tV3YG09Cbq6lggLO4QKFe5h8+alKgXfqCO7oqiryE9OnXdxt5HCpp9Rpw/cF5ZcOoSWFOL1Gz58OEqWLIk1a9aw/ba4fCyBYgBYvCoKJIFiAJgDAPMCZwQSCfzRwUVaP+5no67guY+equz08rSN/EDMS+vHN1NVeL+Epk3qnzCrBtceqtKOurJQVJ/PBWkvFWn9ZJ9dtWoLDh0yQGzs9wCskZ0dC6l0Nvz9vRlXl7AsWrQSp0/boVSp/uzntLR3SEiYhqNHV6sVRUuy6tFjBKKiusLauivE4jhERXli2rT66NnzJyZHuizQ2hHyDQoT2w8ZMg+GhlNgZGTP6FSCgh5ALF6DAwdW5engTv6L3t6X4OQ0/Z+8swk4dKglzMymo1Spn5Ce/hJJSTOwbt1YFpVbkCLUwgp52CgDg5fXXkgkzaCtbYLU1Mvo0aMM+vSh96fj0qVLIFoVGm/lypUZNdGSJbuQlOTAKHYsLSPh6Tk6X1od4Zoljevs2WuRmNgUFhbfIyHhGczNr2H58mkfaPNl/cuoX7/+ug+HDl1EenoWatZ0wvTpIxjg+JxLXn5yBe03N8cLASE3Gwt9CfMbsCPsnyLTNdWhnLpDhgxhvn70d2G8r6Cy+VyfLwaAn+vMfCH9KgaAOQBQHjjjWijusJ8frZ9wGdDGSjdbCwuLPFeHMq0fB2lFkcOXy0Kek35Rm4mUaS8VCW327OUICmoNTU1HxMTEQVubcgevw9q1Qz8iYQ0MDMTYsQuQkPDDPwEPlzB0aGP0799brS+WANDAgT6ws9uTe0DFxJxHxYrHsXnz4tyocq71k+eT5efnD39/Mhm7sjYoEGHAgLLo3r2Lwr4Q7QsFf2zbdgQvXtjAyKgGUlICoKt7CbGxcQgPT4SBgSZGjuyJQYP6F+jwFM6HPC0s8TD+9dc1JCamw9m5Kku/Je+yQGOfOnURgoObwdKyEZNNbOx51Kv3Cu7uE9SSu7zKwcHB8PM7iYCAcFSoYI2OHVswPz4OtqlPBMblaZMJrNIfdbTyBe5wPhsgOdJFlPYRZVrxfL7ig8eUaV+Flxl1QJpwHMILNf1+7NgxeHt7Y8eOHahfv35hDOOrbqMYAH7V01v0g/vWASCBO65RIHDGSZRJc0O+fgR6hDxUBZkReg/xlvF3yGtLqPWjbB60yRa21k+dMSgyE+U3alPRu0nePFKZp69TtZ8nTpyEr+9bODmNgqamLqKibsHQ0B8bNnjIpfSgLBx//XUBiYnJqFu3FksZpc4BRv0iP7wePabB2noPtLRyaGgiIo6gYcM78PCYojTimmtyjh37HadP30V2thht2tRBhw5tmblLCF6obZLPwoXL8Pvv1yGRaMPBwQgdOjRBXBxlGLGBi0szpvEiEyuBGVXMq3nJl5sYqR/C1FuqzomwHn1HffpQtPR4vH4dw/oPRKJUqT04f353fppUeoHiPq/0PdFYKEUYRZISX2KjRo0KPKZC77SSBoWp0Oj7+JRaeWHXFNHPqLofcFcC6r9wHDRHXl5eIHeO/fv3y43M/9Qy/xLeVwwAv4RZ+oz7WAwAcwAgB2d0iNItm0ejCakICjqNdOiT9oYOaNkNnN+KSVMhjCzmZpmi1PqpMy7eH5IP1xISeFKUZk1Z2zQuAn7UlpDtX9lzwn+n59eu3Y7r10MAGMHCIoVRklSpkhOgUFRl2jRPXLgghYlJN2RkRCArayeWLBnBNGFkhheCSgJmlK6M0qvxSE3q15EjBFQPIj4+BRUrlmLUNZTzWNYcv3//QaxZ8wiWlguhrW2KqKj9KFXqJI4d21GoYIDml+RJgJPPhyJwTGuZDmxayz/88APjn5RXaI579ZqA27ebwNi4OwPMiYlnoKW1GBcubFc7dZ0q8yk0MW7bthv+/s8gkZDW9yUqV87CwoXTWGo7YXDJfwWq8hqPIhO8KjL4FHWE+wE3HdN7hfsB/XdO5hQx21tlU7pRMNWgQYMYJdPixYtV5u/8FOP73N9RDAA/9xn6zPtXDAA/BIC0UdHhTVqPwj4QFAFArvUT+hiqqvVTV1tW2MtR0QHA/QfzymPKHcCpTkG1GtSPsLAwxu1HGSeEIKuwx8zbo8Ns796D+PPPuzAzM4CbW3tGUyFcNzTG9eu349y5Z5BKDWFpKcaMGYMYUTWllhs9ejUMDd1hYFARMTEnYWq6D8ePb2frT6h97dt3PIKD+8PUtOk/wFKC6OjuOHjQW+3gFUXyUES3I6/+s2fP4Om5BSkpRIOTDTOzICxcOFZh3lNv70XYtOkF9PW7QyrNgqbmHdjY6MPdvSFatWpVqFPETdf0d3h4OIYOXQJr6zXQ0TFn/ofBwR6YNq062rdvn8tJKOv7yjOWqKsZLsyBcFcQWWqUwnxHYbclz9WBfiM50t/0XdI6I+sGFbpAULCHu7s73Nzc1NbEF3b/v7T2igHglzZjn1l/v3UASJsrD/Kgw5oOXopGK6pC/GGkZeSmXXmRxRxU8Y2zKHz9imp8vO9CLkJ6l6yJiBNvK9MyFVU/C6NdrmWisdy5cwcXLz6ElpYI7ds3ZiTMNG9EU7JixUOULj0JWlpGiIq6BgODA9i27RcsXboGx4+Xhq1tTkAKlcjIEVi+vMdHwRsjRkzDvXvNYWnp+k/QUiJiYn6Cn99y5u/G5cu1LeqMj8bBwbgqgTdUf9w4T4SGtoOtbU6Qybt3J1GjxgMsXDhd7qtPnTqNpUvvQ1e3MhITXyE+PhSJiQFo2/Y7eHlN/SjdnDr9F9aVjY49f/485s+/DXt7j9xqYWHH0LbtS8yc+a//YUFNm/ntb15gnPYGWWqUwn5PUbfHKV5ozXA6qs6dOzNgTsFBdJFYu3YtXF1dC/3CTWMj+pj169fj0aNHSE5OZus8r4s9RY7fuHGDaSn5/rt06VKMHKmYFL2oZZhX+8UA8L+U/lfw7m8dANKmQFoj+uDJ/EpmLGUZPQoy7WQ2I9MTbS7kG/Ulav3UGb9QI8DNxvx5Ahukbc0PaFGnDwWpS/0nExVdDMgXlBehtuzUqXPYuvU+DAw6Mj65zMzfMHVqG7i4tMTcucvx7FkT2Ng0Zo9Se8HBM7F8eW/89ttpHDpUAra2w3L/LTJyMNauHQBnZ+cPun316lWMH78KWlrDoa1tiaSkg3Bx0cHSpR65WixOPaNO9gehlklVEzwBk549p7I0fFpa+qyfaWlk4p6Lo0fXyRU3rftJk35BUFApvHnzBBJJF5iamsHePholS16Hr693gbS2QpOvkGuRAlUGD14MG5s10NY2Y3yFwcGemDr1e3Tt6qpwaeSl2RaajYtCQ8j5QmVNpQVZx//Fs4qoaiiQacGCBQgICGAA9969e2z/JdcJAmvkJlFY5dy5c4y0m9bs0KFDlQJAyn1NkfPkj/gllGIA+CVXkLPEAAAgAElEQVTM0mfcx28dABIIo82egAgFaNAhrygXaGFMIx2EdEBzbSOPLP6StX6qyEXoW0bAj4CvbIaCwqaaUKVfwjqkraAoYeobHUIU6OHtvQ7PnoVCR0eEHj2aYvjwgblcizyzSu/eU6GtTT5lZVlzsbEPYGq6B1u3/oJVq3zx558OcHDIARtZWWkIDZ2KzZunsPU2ZMh8iESjYWBQCXFxJ1Gq1BUcOrRZLu8Zcd7t3OmPuLgktG5dDwMG9P0g2EOR+U0RibIw0EMdEzzJafhwdyQkuMHUtBLCwy8gPPwKKlSIwO7dGxUCOdL6kN/krVvfo3RpVzg4lGJ1Q0Lc4e3difkR5qfkRYhMMlm1aiOOHiUTfB1oaLzGd99lYMUKj1wzpCrvzEu2heVHyL8RzjpQlPuQKmMuSB0OYoVgnNqjAKz+/fujRYsW8PDwYJc/Gi8RulNeZ6J9oQtyYReiJaJ3KtMAEgAkQvf58+cXdheKpL1iAFgkYv12Gv3WAaDQcZmigIXplAp7FdCmSBpH2vTUjfD9r339CiILYQo0GoeQR5EfrLLp64Qpqz5F+joK0liwYC0CApKgoSFFlSrmCA+PQHi4C+zsfkJmZgwiIhajdWsDBghfvXoLU1NzdOzYGGfPPoKDwyZoa+ccXKmpoUhJ8WLasJcvX2LatPXIymoHfX0bJCScR/Pmmpg5czzTflBO2vXr9yE0NAp161bB5Mkj8kxFps48yMpWmAmG3k3/n18t061bt+DltQsvXkQjLa0cNDWJWzEUrVuXgKfn/9v7Erirpv39L7nUlWa9lUrGZsqYisotingNqZRLg6EJkWRIk+IKdQ0lRIMmcpUGdf90uRrplh9lyr0UkQZDiUTD//OsrNNqd4a9z9nzedbncz69ve/ee631rHX2fvZ3eL53pHSzjR79vMyZUyBVqlyupoIxfvXVALn//lZpS7ftI8+7lIwSkqj0HtKua+yRVN8R9IFYM1icUKEFWcC5hnmkw9Ya7mBnzTSJxbFexB/bGYMbx5gk1ipVA9cqtP0eeOABKSws9DXezwkBRMY41gM1tjFOxCea1n83cHLrGiSAbiGZp9chAdxXmB4NWZr6gejmdjD1BHFdneWbD1Y/XWPZCdEAXmaWsQ7QNxNL3E7Que++4bJsWRWpXBmVQ/bKmjVPycaNL8kZZ7wlhxyCah575b//HSkbNsxSyRy7d7eSvXvLS/Hi70ipUiukfPmuUrUqtAT3yLp146Rlyx1y++3d1TZas2aNzJz5umzevE0aNqwlF120T+oliKatfth7wDAX4e9x48bLY4+tkaOPvkWVvzv88CKyfn1fefTRa+TUU09NOj08XPv2fV6OOuoGOfLI42TTpn9LmTL/lGeffSAtKVu4cJGMGjVdNm36SSpVKiU339xO9aGz5v1I+sm0XtnGEaaqhpGpv7D9PRWJxe/Hjh0rEyZMkGnTpkn16tV9H7pdArhs2TIVm4iXjFWrVsl1112nxgtpmjA2EsAwrkqExkQCuJ8AIhZQV9FwawlNPUG8EcPlrGPfcGNE06XczGB8jCPKVj+t96UtGtlWT8H5mhBqK6FJCK16edmsG0hEYWFPOfrox1SmKNr3338hS5deJY0bvy2HHnqEIoDLl/9VfvutpIicKMWK3ani/XbufE8qVXpKjjtuh2zfDoHvPVK3bhm5555eSu8xUwMhQ63TJUvel4KC0lJYeJFrFkBr38msZelISybh72efHa9iGKtUuTLR1Vdf/V3uuOPElHV4ceBbb/1bxo2bI1u2/CS1a1eRHj06SrVq1VJCtW7dOrnxxgelWLFeUrLkqfLDD8tl587RMmrUXapuby57K9P65PJ3M44wmWSSLhmoJXfCQGKznW+qeD+Ifvfu3VtJCz333HMppYKy7dfueXYJoPV6CLto3rx5Ik7cbn9+HUcC6BfSMe0n3wkgHoB4MKJp9yyIV67NtPqB+Gk9QZBMED4dBxelDF87mOChp0WdnVj97FxbH2OSFjfK14FYtm/fW0T6SIkS++oB//jjB7JmTW/585+bSenSl8muXT/Ixx/3laJFz5FffjlNihXrpBIKfv31PalS5RUZOPA0qVmzpiIjVatWte3eGjbsUZk5c60cdtiFsmfPV1K8+EJ57rkH0hIiJ1jhWJ3ooeNO08WWZSItula0dl8PHvz/pHLlwVKkSFHZuXOLbNp0j4wZc3tKORhzDTEeO5bQl19+WUaP/kmOPfYmhfmuXbvl66+fkD59qknr1q2dwhHY8Rpba4Z8qhjNwAbqoGPz+24NnwFxhwWtTZs2cscdqcMCHHSX9aG5EkB4h3ING8h68GlOJAH0AtU8uiYJ4H4CCOucll3IZQskqyKiY4a0SxT/13Fu6AvnxMHqhzd+zA0PAy+zqc31QX+myxgPWKyj1WWcLmNzxoxZ8uSTi6VYsUJFMnbsmCGdO58mmzf/IIsWrZbSpY+SihWLyezZ38jGjSDwD8rvv++VokX/I1WrTpJp0x5xLGi8fv16adPmDilXbpzKUEX75pun5Iortkm/fiCkuTdtic1WTiRdNiz+NmLEGHn77a+lSJHjZO/eT+WKK+rLzz/vkEWLVskxx5STLl3aZJ3coWf/6quvymOPfS2VK9+srMH43qxfP1z69auT1tKYO3reXMFMvgEBNkMetGvey3AHt2alXyysOoX4/VtvvaVI3+OPP64saF5kS9uZh37BBwFs1aqVsuRh/yQT+N+0aZOKFUUSCF7aIVHTqVMn9TI2ffp0O935fgwJoO+Qx6tDEsD9BBBSAbh5ZRvwm8rqlyzWT9+YQAjxdzS/Ex/c2smmFcCOjpxb/aa6jiaEZlF7TQhNl7H5UMI5cPfMm4dSa7ulRYuzBZpgprUMJH3EiNEyYcIrsnnzTilatECOO66oDBhwQ1ZixitXrpSePcdJhQrPJKby3XdvSJ0682X06GEHTQ8JEHPnviZr1nwldeqcIBdd1CplyTfMB4ktiGM65phjlHXSjYewfpHRhBv/IrkC0h4nnHCC/O1vo2TVqqpSqlRr2bFjnYiMl7FjB+RUlQXz6Nq1v/z6a6GULn2a/PjjCilZcr6MHTssEjV89UKa3xNrdqw+JlW4gzXT2I21zOV7mOrFAuMH6QNpR7wfXPRBNsQdosqIxgtrgJ/ffPNNNTZUC4JWJxKDUFP6qquuUjG7mF+FChXkyiuvZBJIkAuYoe9DQjy2SAwt3wmgviljsWC9MlXqnSxgOqufnVg/qxVLWzrMkkpB3/ST4YFxatz8KFDvZE3MB6/pdtMWQiu2Ols5E4mFfAuqjmDNIBejqxo4HRvcSpdeeoMUKTJISpSoJ3v2/CZff32f3HprdfnrXzsmLofxvvjiSzJw4FOyffufpVSps+XPf94p9evvlKeeGn6QbBHWZObMV2X48Amyd2812bv3W7nggroyaFC/g6yy3333ncA6gpcfaA+CxDlpmhBijJDy6NFjlJQvP06KFNlXCu/bbyfIZZd9K/fc08fJZRPHajkRuBOnTJkja9Z8LbVrHytdulwVOLlwMiHgBIyBk5PvSaqXmWxLLzoZc6pjdUk36/cE1rUePXqo78OYMWOUF4DNWwRoAfQW39hfnQRwX8wamq5O4USHyonVD32YhCmd8G66xAftHgqSEEY5YSWVWxPrA4sfHmx+iVMvWLBABg16WnbtOkH27Nkop59+tDz88IADrNDjxr0gjzyyWDZtukAOP7ya7N49RapUaSSHHPKGPPpoe+Wy0g0PZ7iW27e/XY488iEpXryu7Nq1XTZuvF2GDGkpF198ceLYzz//XLp16y8//lhL9u4tIUWKLJZBgzpLy5Yts7rvQRrm1lsnS4UKY1TSDLKpN26cIQ0bvitDhtx5UDWYdJ1gjXRNYhCmKGvigfQhvMStkoemZTsb8e+sFvePWFIdwmKN94PcEdyl0PEDCXQ7Sz/bMcf9PBLAuK+wx/PLdwIIeHFT0//qaiCZYNeWQ7zVg5DBbYybnraIJLP66eSIbGL9MhFCXbs007jd+LtdEutGX15fQ1sztEXF1IV0UlEjl3HCCgdXbZkyZaRu3boHuGrxgL/kkhtUibivvz5WihU7XnbtWiWHHvqoFBScJH37Vpa2bduqfacJE2oM9++/QAoKRiaGtXHjP6R58w9k2LB7Er/r12+IvPnmiVKxYmf1u23bVkqRIsNk7txxtpIzrHPGd+Gyy66Xn3/uKuXKtZSdO7+R7767T4YNu0IgsKszYTPFaOYat5jLWrh5runy9SohynTJW4XV3RKoNl9c8d0HIdeZ1+j/tddeU6LOzzzzjDRuvK/iDZs/CJAA+oNzbHshAdxPALW7CbV60zXcBPFGr4OfdUBxslg/ffPEwxHn2S23lWnDBUUItZSIG9aMTHP08u8mYbLWI7bGueHBit+Zbje/LIR4Ibnggk7y0093y7p1EG4+VYoU2SK7dvWWypUPkXHj7lbxfdhfaHg4f/TRR9Kly8NSUDBRSdigbdjwiHTqdLj07HlTAtbWrbvIjh33SPHitdXvMMcNGy6TV155VKpUqZIV/HADDxr0hHzzDeSOdkuHDhdK9+5dDyC16WI00Sn2WDYvSVkN2KOTMEeERlgTJDzqLnFZ9JssnAQvp9kIVOPCpgUT+0t7HvD7Bx98UImZo+ZupUqVvJ4er29BgASQWyInBEgA92XgapcmiB1EQJM10+qHB5RW7Ldj9csUV5bTIv7hWjbj3EytPFPPLdt+9APNLFMVpAs623noBxoIE8ZvrUySat2thNskhJoYeoEH8G7SpFDWrDlNdu++VH7//Rc59NBZUqbMUrnppivl+uuvUy5rc39hbLfccrcsWbJXjjiihfz002rZvXumtG3bQi6/vHVCpHmfBfB4qVixq5r2tm0r5LDDHpQ5c57PygKosQPpQYwkvkeorZ2pacICC6a2nOMcMxPWL8Kdaax2/h42C2Y6rcdUSVF6nqnqEqOkJWrrHn/88TJy5Mic6jjbwZTHJEeABJA7IycESAD3E0A8uBDInEzA17T6wd2rRVv9tPo5WehUWnnZyEtkWzPWyXj9ONYtl5yZ+KCzYTUhdDtpBwkafftOk59+Ki9bt66V3bt3qHq2tWodI9u3I1ZRpHXrhtK3b68DHsKwPs2aNUteeWWBrFz5kZQs2Vb+9Kej5dBD/ykDB3ZQ8imIAeze/T754YeT/4gBXCqDB3eVCy+80I/lSPRhJUwg0qY0SpAWWKdApCJMTq/j5fFmDKx2y1sJNyyGsD4nq0uMai6I9evTp4/S+fPixcfL+cfp2iSAcVrNAOZCArifAOJBA6kNxGLpFmarn5Ptko4QpqqmYbpJU8lWOBlDkMfqZB3MyQuNQithccsCO3nyZHnyyV+lSpXusnPnRoFi0IoV18mRRzaVGjVulr17f5GNG4dLr1415dprUcbuwNahQy/ZsKGjlC3bVP3hxx+Xy5FHPi4zZjyjYlZhyYH8DSyiDRo08DWzVlvdQVbTxcjZiXPzo150uv2rvyvJCFOQ+95O35oQmskl+B0aXnSR4IFsd1hzIco9fPhwGT9+vJx55pl2Ls9jPESABNBDcPPh0iSA+2KO8MDGBxIfsABqK4SO9XNq9XMi9RDEPstECDEmWADsukmDmIPdPrVVxs+4snQxmk7K1y1ZskTuuGOqVKz4dylSpJhs2/aZLF/eUerWfVXKlauIQoKydet/pEyZp+XFF588ABI8xJs0aSclSoyRokVxLPb6Ntm8+RpZsGBioDIducTIeRHnZncvJTtOv1zgbzosJJfrBXkuLIK452lPAUghtPHwkgDdPHyXkPBx0UUXORY+tzMvxBKOGjVK3n//fVWZCffmdBnFuF/37NlT5s6dq45DlvuTTz4pmeK47YwlCseQAEZhlUI8RhLA/QQQDxZYRHDz0G7PsMX6ebWVTEKImy6wMKtp6BJgXvXvxXVNkuFW8k2240xFuK16btbr4wE8YMDf5M0318uePbXl99+Xy3ffrZVTTpmXqFv83XdvSbVqL8u4cY8eNLzeve+Td9+tLRUrXvuHLt90qV793zJ27COBue60yxcPbKyLG5IhdvDVNbezXcNk5+mkKF1DPKruUDM8wmrtR4WMbt26Sbly5dS9cfHixfLBBx+o+L/Zs2erJCS32uuvvy7ff/+9skgjxjATAQThwzEgjphDu3btlCLDzJkz3RpSqK9DAhjq5Qn/4EgAJSFPoQkgyA4eUnGz+mXajfrBjOPwYNbB+fg9PnhQZyIsmfrw6+9hj1u0Zmqmk0ZBcsTSpUtlw4YNcvLJJ8usWW/I/Pk7pVSp9rJr11b55ZfnZPDgK5LG7q1du1Z6975fNm9GWMOhUqrUtzJixD1SvXp1v5Yi0Y9bMZh2Bm7uXR3nlqkajJ3r6mNw/VSaeE6uE4Zj01ljV6xYITfddJMMGjRIWQI1wUWoDKzTTZo0SVmNJpe52andi8odKNMGMlqnTh3VHX6uV6+equpRuXLlXIYQiXNJACOxTOEdJAngPgKID27oePPE27wTXT+vM3y93j3mwyxVLFYywhJGQhjVuMVUhAVrj7+ZVhnEzE2cOFXmzVsmxYsXk44dW0nLlhemtOiBQOJBDivZaaedlnWpw1z2IeaQTSWMXPo0z02W+AAyY2rl2c00NuNJTU08t8bq53V0cpu1VjTweuGFF2T06NEyZcqUBMHya2x2CCCSnNq3b5+QQNJjw3cFsYqtW7f2a7iB9UMCGBj08eiYBHBfrBvKcuk4QFQCAQnU/9fuUP32qx8AWhQVFsOoNlieQCicJkeEkRCaFsyoP5i1BRP7CkQ7Wfk6/D4KLscwWmNTJT5kEv/Wc8FxsJJHAf9U96ZU7mvcD/v27Stw/U6cODGQest2COCkSZPUOGEZNxtq+I4YMUI6dOgQ1duy7XGTANqGigcmQ4AEEPpn2xKCrXBtaPKgNcl07JDpwoqD1U9XJnFjLulcbjqg3I1YrxR7WAWnw9LlVcUFv+4eqdykpgVLZ2tma8EKYi5hzyIHvum0HrF38XeQo7DPJdP6pnNfQ7/x2muvVeUA77333kTFj0zXdPvvdgggLYCCF5BD3MY+StfL68m7sVAkgAfGAIIMgrDAAmhaWLSlLA5WPz8smH4RQj/m4sb3zM41MBdYY7HXMmWRWy1YftaEdXsudq7n9zGaEGqyDWuZtsZmo6Xp9/hT9We64hHmoku64fhFixbJrbfeqmRe4D4NklvYIYCI80NmMjKGdQwgfkaYw7p16xgDGJZN5+E4SABzBJcEcF+pIy0Fo+up4kaJmyM++Bl/d8NSluNy5XS6ngdIhp+SKBi0HULoNEtTu7Di5I7Ldi6mBUsnPeg9bBIWPx7qpsvXLB2W0+YN6GRzLrD86YQo/F5rPUYlMSpV9jXmMWbMGJk6dapMmzZNTjrppIDQFoUpvtcggK1atVLC/MBXl9u0DuySSy5Rx0MvE/v96quvluLFi8uMGTMCm4OfHdMC6CfaMeyLBFBk5cqVSt6gfPnyCasfbkSQI8CNBzcWtCBqwbq15UzrUtCSKHYIIbBOFeNmZi2GYS65rJGZtGKtSZzrddO5NL0oX2e6FuPgJtVhBanmYkrPaGJoSiel28O5rG0254IkIQnHGiKB391yyy2KeI0dO1aRpyDbhAkTpHPnzgnro46/fvPNN5W1r1atWjJ//nxp1KiRGiZ0AHv16iVz5sxR54AQQgfQTgnCIOfpVt8kgG4hmafXIQEUeeaZZ5SyPd42zz77bGncuLFyITz88MOyYMECqV27tiKBZq3doKwr2WxT01KGh5lXsXjZjE2fkypL0+pu01YMPFzd0pDLZdy5nGuWQLNTkziXvqwuTViwTCu3xjlbC2GcMmPNF4xMrnhzTVLtYauFMFuMs1l/8wUDc4HlX7cvvvhClXKD1ey2224L5X0hmznn0zkkgPm02h7MlQRwH6i4UUJ5HoKigwcPVmr4p59+uhI7Pe+88+Tcc8+VsmXLqrfMTAHjOpPQzxt9sq2BhzJc2njYR81SluxhqucIfGHJsCvb4cHXJqdLmq74IJNWtIVQu4y1S9NpjFucMmOtdYlzeVlKlriDjZMp0zinzWWcnIqUY1x4se3Xr5+ylp1//vmBxvu5Nd98vA4JYD6uuotzJgHcDyZcIL1795YuXbrIAw88oIgTSiC99dZbsnDhQvX/hg0bKkIIK2GpUqXSEkK/bvTJtoO2+sXFUgZCrh+e2vXmprCvi1+ptJdykujh15h0P1aXsUkIk5Wvs2aTwroU9EtPLpjpkoFekfJMcZpuuuVTxWFiTSGRMm/ePBXvhxq/bNFFgAQwumsXipGTAO5fBgQOlylTRqnbWxtu3igTh+BkxKMgYw4PO8SigBDiX8SdWC2EZkC+aVnxynrlVUxZEJsVc0klVZPOZWySlTARkjDq4aVbVzPGzZr0AIyxNmhR11zU3xm8NPlpKbfjltdxhE6+f5rIWmMXoXCAkm7wZKDeLv7OFm0ESACjvX6Bj54EMLslwM17y5YtyjqID+pjwgICVzEI4TnnnKMCqpMRQlOyw9TIy5WsaIIBt1XUi9I7tZSFmRDGJTkC88AeA8HQezisSQ92v9XaTaqJbC4uX7t9ZiLd+qXRJN2mNyHVGE0ia41d/OSTT5RnA2XdQAJzvde4MVdeI3cESABzxzCvr0AC6M7y4+a7ceNGRQZhIUSdTOhsaULYoEEDRcq8IISm1S/q2ZdYDTfkXZLFX5nCyXig+lFJI27JEVomSVvKTLJi1jMOqxXW/LanqoThzh3BnavYzTTGfkdGr5XI4vezZ8+WIUOGqCxfhLCwxQcBEsD4rGUgMyEB9AZ23Hihqq8J4bJly5TUDAghXMxnnXWWcsFoQmjqi+Ghapes4Fjo+uF4rzNJvUFq/1V19qV2xUGCx60WBCHUrrio60diDewQ2TBmwSbbP9bYRTf3mVv7NdV1tBVWKxJo0o3f44UGXgjtVcC9YejQofLOO++o5DaUSGOLFwIkgPFaT99nQwLoD+S4QX/11VfKOojPu+++K+XKlUtYCEEItdipHbICwgeCgdJUXgWt+4PMvl5MIuuH+9oOxtlaCDWRjWL2dbI11xpyTolsJoy10Lqf7kg7RNbPfZ9LXyaRBenD/6GHB0mXM888U8UsQ8UAElfwRnjZBg4cqCyMiDOEegJiDCGflaw1bdpUli5dqu5bGDPWH9VH4Jpmc4YACaAzvHi0BQESwGC2BG58a9euTRDC5cuXS6VKlRQhxOeMM85QhBAtmWVFjxo3UWvZumBmlF2vYYmP0xibWo92rbDmzP0mstmhbu8sM7TAqiFn7woHHpUMYxzhV7Z8nORqzJcMM94PZB0u34kTJyqR5M8//1y5hhGT3KZNG+nevXs2S5f2HOilQk4GmcUnnHCCktFC/2vWrFFhL9bWrFkzFSeN49hyQ4AEMDf88v5sEsBwbAHc0P/3v//Jv/71L+U2XrFihVSpUkXdKPGpX7++elN+7LHHpHr16kq7CxYqHYMVxWB8XV8Zcw9bJmk6spIscQfH64zlOFhk3dTDS/UNA2ZWLUL8zu2KO2F5yXDrTpNqbTBPuHoh8wIChpq4+N3HH3+sZKyA6/XXX+/WMBLXgZXx9ttvVxU50DA+vMxiHB07dkxKAPGSi7hEttwQIAHMDb+8P5sEMJxbAA/Gzz77TBFCuIxhIcTNHB+4W/A2DyKClir2ShMVnfAQlpmaZMmpWzGoOWSyXumMTSeVI4KaS6Z+g4pdNAmhtsSahDAbnTwzmxxuUFwjyi2VOx5r1r9/f3XPmDJlipJ68aPB5Qs9VLh0UUVJtwsvvFDq1q0rjzzySFICuHr1akX+CwoKpLCwUI3daze1H3j43QcJoN+Ix6w/EsBwLygegChVd8cdd6g6l4jtgQbhBx98ICeffHIiqaROnTqJh1uYCaFTeZewro4mK4jBxENZNz+0Hr3CJGyxixrjZCUYTSthqhjCVGLIXuHn5XVNKyaSvczEFagPdOrUSWmRwqqmXwy9HI++9vr166Vq1arKygjPhG7t27dXuqi4d1kbEuJq1KihiOOqVatUOTqcO3XqVD+GHKs+SABjtZz+T4YE0H/MnfSIkk24uT///PPSokWLxKkgUniL1i7jDz/8UGrWrJkghCiarvXC8PCwynXgb9YapU7Glc2xUahJbHdeZnyclkQx3ZlWrccgEh7szgXH+eHydTKeVMfaKV+nE6QgWRMHWSTsNcTxYe7WUAkkk/Xo0UMRvyuvvNJ3fb9sLIDWtUW1pebNm6ta7AifYLOPAAmgfax4ZBIESADDvS30zT+TewQP8P/7v/9T7mJUK/n0008FVkGtQ4g3bCsh1KQQ5+JvdsRms0ErGVnyM/MzmzGnO8ckS6mkd1LFt4XNQohxamIexdjFZOXr9NphPrCUBS3unMv+03tNi7vr7w3Wbdy4cSrzFi5fvPAF1ZLFAFasWFFGjhyZNAYwFQEEmWR1EmerSALoDC8ebUGABDCeWwLkbuXKlYoQIqkECSannHJKIqnkxBNPTEoInVYfyIRe3LJis030SEcItSXWq/KAqdYobC7fTHsp099BllAzWmdv4/+6nrHf1u5MY7Xz91S1iWHZ7NOnj8ryhcQL9EWDbIjzQxbw3LlzlewMrJGTJk1SL6HWLOBNmzbJe++9p15M8Td4LuDhqFatmkyfPj3IaUSybxLASC5beAZNAhietfByJLDyIJFEWwjXrVunMouRYYyb8XHHHWeLEGorYSarihmzFEXLknUt3I5dDJoQRsXla/c7kYos6fAH09ptZsxjP+P/YbJImxZzq/wOtEQRM4d44Lvvvjs01s1BgwbJ008/rdy4kLDSOoAYL6yT8+fPVzGKX375pVx11VVKIgZ7EOLUcF0zCcTuTj/wOBLA7HDjWX8gQAKYn0kkt2EAACAASURBVFsByQuIH9IWQlQtgYCrrlSCwG79UDTLUVkthNqlaT5Awyzvks1qm8kEcPlmIr/Z9OEXITQzsONAzDVZ0tVjoImZriWLh9WEMAzl61IJVWPckHKByDPkVVq2bBkq0prNnuc5uSNAApg7hnl9BRLAvF7+xORRTg4lozQh3Lx5s8o4bty4sSpdd8wxx9gihLggrDFRkXfJRBZAlPHxO5kgkySKtsQ6sVyZLt84yNVosoQ1zLZ6TKqMeavL2AnO2d5RUglVY56wqL388ssybdo0JbbMRgSAAAkg90FOCJAA5gRfLE/WRAHaXjrLGPFG0PnSSSVw3ZgWwq1bt6qsYpyLprNek1kIowBa2KyYuRJCP6yYfq5rtuXpMo3RJIRafiabijCZ+jH/blplrS8aiGns2bOnqvYDSZVMyWBO+uWx0UeABDD6axjoDPKBAEIz76677lLBx9DMeuONN1QljXQNZYruv/9+ZVnADRoPAcTdTJ48OdD1CqJzzB8PosWLFysLIWQbEOvToEEDRQhhoejXr588/vjjcvHFF6shgnDouCv83Ux00HFXQczFTp9BCSHbGZs+xi4hxPE6ccVvK6aT+dg9Np0ent1rODlOE0JTixDnu1W+Lp1VFolbiPfD5+abb/Yk9MAJFjw2fAiQAIZvTSI1onwggJ988okiL0h6gFvz9ddft0UAocEHssN2IAJ4aIEAAsehQ4fKRx99JKjveeyxxyp3MUhhmTJlDrAQakKIf3G+2+W+3FijKGfFakJoFU3WuMSB/KWKj3Nj7e1eI1WsZjb7GfPRWcumCxt9/POf/5R7771XnnrqKWnatKnd4fG4PEOABDDPFtzt6eYDATQxQwC/XQsgCWDq3QbNwauvvloRPdQdRekpkGVYCBGsDiLSsGFDlWWMOEKo/psu47ARQi1Xg/3hVaKH29/ddNfTLlLMBx+sR1iJtx1cUsXH2TnXy2PsWmKtMYRaexGuXZBz/Xes08MPP6zuUajri3rgbEQgFQIkgNwbOSFAApgcPriAoW+FN3N8QGaGDRum9KrYRCWMwAII17q19BQeij/88IPSHwQhROk6kBAQQRBCYIkyUXjo4VhTqsNvC6HpUoyDlSzVfFJZCLOxXPm5/60uXxAmPxIysp1jJpw1GYdb3lrSDXG0N954o0BE+YknnmBVjGwXIY/OIwHMo8X2YqpRJoCdO3eWCRMmJIiEFR+4TpDEkI0FEG7No446Sr2Bb9iwQfr27SuoYYl4Qqu4qRfrEqdr4qGIrGJUKAEhhDseWcI6oQSEEMHtmhBaZWe8slyZLkU8jP2soerF+mqtQvybaT6ZiLcmhkGSLVN7EfsDY4piM8vX6brRIIKQXsJ9Bi9G+H507dpVJXzccMMNnpLcgQMHqgoiqLwB6Set2ZcMWyR/YUwQecaYEeML0eegxaejuA+8GDMJoBeo5tE1o0wAUR8TqvipGqwFIHHZEEDrNfHGjpve7NmzVd1KtuwRAPn49ttvExZCZBsXL148YSFEcglItkkIzVrGOD/XIHyzLjHIUpBEJ3sk95/phovUWsvYJN5+E0Iza1nvBTdwCuoaZkk36C8Ca4SYQMz5v//9r9r/F154obRt21ZZyQsKCjwZKtzLIHDz5s1TcjLwdCCEA8LMyV5sQfjwXYE7GvuhXbt26mVt5syZnoyPF3WGAAmgM7x4tAWBKBPAbBbTbgxgKgI4a9YsadGiRTZd85wUCODBAmsIrIP4wNKKmEEtSn3WWWcl4qRSBeHbrbGL8/HSkMwFF8UF8tJFGgQhxHzilLWMPaXjMa3C2yC5KJu2evVqadOmjaxatUpZyeFlANGaOnWq61syWd3eSpUqKXHpjh07HtAfqnYg5AXjQV1xNPxcr149VdGjcuXKro+PF3SGAAmgM7x4dJ4SQIj54uGCt1y8/cI9rK1IyTYF6lJCKgbJDZCOgQsYsWy4SVOLy9uvEdYJJaTgvgchRMWS8uXLK0KIDwghHqZomQgh1hikHxa+uJU/8zsr1iSEus6um3qP6SRRvN1x3lzdJOfWkm5btmwRhLCceuqp8tBDDymdP90QP4t7To0aNVwdGFy+eLGCxR2anrrB8li3bl0V82w2vOy2b99e4GkxG2JlIUrdunVrV8fHizlHgATQOWY8w0AgHyyAqHuLWrdWNx9iYQYMGKDQwBvuNddco5Ia0AoLC5UlCjINpUuXVm4Z6ALiDZrNXwTwIP3iiy8SVUpQ0xhWCx1DiDgmxBQmI4SwsqCBBILA6KxLL8q5+YlKGFzYbhLCOJJzVNcBRiB/Zvwi9EiR7AGZF2TS+xV+sH79ekGJx48//liqV6+e2K4geUjKgtC02SZNmqRefBEDbTaIwMNi2KFDBz+3PPtKggAJILdFTgjkAwHMCSCPTs5GnBpDcRLA7dHQA78sHqoQydVVSlauXKkebJoQQu9RW1Q2bdqk3FUnnXSSegiDaHhd2cFLgKwuX018vezT7rWzJYRaeDsOtYmBFfYYXhyx38z4RazdlClTlGA6yBWsf342WgD9RNufvkgA/cE5tr2QAAaztNmIUzsN4A5mZv73CuKBIHbtMoZGIQLc8YGrqmXLlirTEVY/PITxgDYFk6NCCP12+ea6kpoQaqzxf6yBGa+J0AxYM5GIY7pBc+07qPM1mbVKCuH38C7gZQTkD/qZQbRkMYCQnRk5cmTSGEB4Tt5///1EDCB+Pu200wReFcYABrGCB/ZJAhj8GkR6BCSAwS+f3cQUJwHcwc8quBEgyaNHjx6qbB/imyC1AZeXTiqBu1+75DQhNLOMQQjNLGMdQxjcjPYlEsClaBUODnJMTvtORghxDcwJH6xJVF3zOrkI6wSrnykphIz3a6+9VlXLGTRoUKByNojzQxYwZF1wP0ESCgjpp59+mjQLGOUvMSd8lzBHuKyRsTxjxgyny8/jPUCABNADUPPpkiSAwa+2HQLo1H0T/KyCGQHIH+I1YXFBFmXNmjVVHBZc7kgogTg1CGGtWrUShBDHaOJhEkJNCjUh1KTQT0IYt6xl7AqdFQvSB0zTWQijQAi1ZRZzM0u64f9IuOjVq5c88MADctlll/kW75fu2wcS+vTTT6tyjmeccUZCBxCJV/hezJ8/Xxo1aqQuAR1AjH/OnDmJeuggkIgZZAseARLA4Ncg0iMgAXR3+bwSp3YawO3urKJ1tVdeeUUJ1upMYevoQTgQiA8yiA+sH8iC1DGEsBbaJYQgMCCIXgTyY5yw+uns9agKIWv8M8UvWgXAk7mMw0YIU+kvYuwQW4ZQ/bRp0w5IuojWt4mjDTMCJIBhXp0IjI0E0N1F8kqcmhZAd9fJvBoe4itWrEhYCD///HMVoA9LIkjhiSee6IgQukFSdCwZkjzMWrHeoeDtlbOJXwwzIUynVwjSfttttyn5lOeff57WMm+3Vl5fnQQwr5c/98mTAOaOYa5XsOMCRh9OArhzHVM+nw/y9Z///CdBCBG4j8xiEEJ8TEkhEAEzflBnGetEB61DaBdP0+Vr1Y6ze42wHedGlRLMKR0h1JVK3CDfmfBLp1eI5AjE+0HYGRIqfown03j59/giQAIY37X1ZWYkgL7AnLQTp+LUTgO4g5tZvHrGOr3zzjsJQghdNMROoYZrkyZNlASNdgEnI4QgAZqgpCOEcdPC87JKSVCEMNUaYa4IJ+jTp4+SeUG1IC/CAsL6zYKlE3GOKGunG16kjjnmGHnuuefk0ksvDevQIz0uEsBIL1/wgycBDGYNshGnxkhTBXAHM4v87BUuPhBCrUO4efNmVZ1EZxlDpNpKCLWVEARCS6GYVqs4unyBE+aLyjl+xC96bSHUyStWtzz6Bel79dVXVbwfLMT51rDWIHuooPSXv/xFTR+Zw6h1jHtdPpFhP9eeBNBPtGPYFwlgDBeVU/INAe0OXLJkSYIQbt26VZXa0oSwoKAgKSEEKQR50A3EAokrUXcbapevVQjZt0X5oyNtjcV4NNZ2rbHmWNMlr2zfvl26d++uSC4ya6FnmK+td+/eqqb3Sy+9pCCAdbx58+Zy33335Sskns+bBNBziOPdAQlgvNc3m9lBPBk3bV0MfujQoXL55ZenvNTgwYNVmTzErOFhibd96IfBApBvDfNHFYjFixcrQvj2228LSEKDBg0SSSVHH320wgjSNLCOoC61rlIShczXVGuaLjEiDPvAJIRaDDwTIcQ5SOZIVtLts88+k06dOknXrl2V7mTUiXuua4Rs+nr16qn7BmodI5Fq7dq1qmwjmzcIkAB6g2veXJUEMG+W2tZE4doEIYGGHkgc3Fqokbxo0SJVASBZAwFcsGCBIjtsByIAAgG9NWADHcKFCxcKtAqrVKmiYsZuvfVWueeeexLkIVPmq5adCRvO6RIjwjZWPZ5MhBAkHWsFYmct6fbaa6+pOuKonwtLL9s+BGDxQ/wjxK9B/igY7e3OIAH0Ft/YX50EMPZL7GiCXbp0Ebgw//GPfyTOu+KKK6Rs2bLy7LPPkgA6QvPgg4EtMAYZhFUVZetARBo2bKgshBDgLVWqVMJlnKmcWhgIYVySV0xCiHg//B8NgtV4wYFWJIj7gw8+qAg9XJ1+W7fCbp3HfePOO+9U9xBUGEEZRjbvECAB9A7bvLgyCWBeLLPtScLK165dO+nXr1/iHDzwcGOHNEoqCyAylGElwQdkZtiwYVKtWjXb/ebDgbAmwUWGrOEXXnhBEBsIkvH9998nRKlhaYXFCRnGmhAeddRRBxFCnVQCggj3sZll7GfAvU5eQewiPn727cWesVZewXxABmENX758uarhiw+yfWHtwh73a85RsM7jZQD7G3sBepps3iJAAugtvrG/Oglg7JdYTdBuhRKIHkO/7KabbkoAM2bMGBkxYoSsWbMmKVgorQaSAusIJFJw/rJly1SMGwgh234EEBt4zjnnpIwXAwFBVjHcw7ASIrkED1O4GfEBuUbCgSYd2kJoJYRmLWMvCIpZ+xaJD7CSRb2lE6tevXq1dOvWTdWWRvv3v/+tCCEsgHAH165d2/PpR8U6j3hXlL276667PMck3zsgAcz3HZDj/EkAcwQwIqfbrVCSjQXQCgGsQiVLlpTZs2crKwlb9giAaCGeCmQQH9SWBdnWGoR42IKAJSOEIIU437QO4udcCSGIEhJdcB1r7dvsZxrsmanEqoEf3K7Dhw+X8ePHy5lnnpkYKDAAQYfb3o8XnWy+m4jP9dM6DzIMDwKSm2ApZfMWARJAb/GN/dVJAGO/xI4mCCsDys7hoadbphjAVARw1qxZKiCczT0EQEi+/vrrBCGEpRUPWl3HGHqEZuk400LoBiFMpYXn3gz9vVK6zGXMdeDAgQLrH5KikL3tRYuLdR6uX4Q5/P3vf5cOHTp4ARWvaUGABJBbIicESABzgi92JyPOqFmzZuqBd/HFF6ssYJS2QvZqqixgiL+ef/75KlFk48aNygWMWLZVq1YpdyWbdwiAwEB2Q1sI3333XSlfvnyCEMJiBRcyGo61ZhnbtRBaY+OgWRj1li5zGW54ELPTTz9dJX3Ape5Vo3XeK2Tjf10SwPivsaczJAH0FN5IXhwJH/3791duHAS5o8QTYnp0q1OnjpKG0TE+hYWFKuYPLrHSpUur5AXoAqJ2MZu/CIDUfPHFFwlRasSpoUKDthCC0GjypgmhWcsYvzPjB+Ey1lp4+BeuTj+qeniNmo73Qz9WN/aKFStUDCysf23bts3ZZe7WXGiddwvJ+FyHBDA+axnITEgAA4GdnRIBXxAA0fnf//6nCCGshCtXrpRjjz1WkXR8kJWsEzhMQqhJoZZCQWYyXMthkJ3JFTgd74d5m+5yzBXZ2aNHj5YpU6YIXnTC1GidD9NqhGMsJIDhWIfIjoIEMLJLlxcDhxVm7NixKi4R1qtRo0alzLj88ccfpWfPnjJ37lyVZQsX9pNPPqkSUtj2IQBCiIoNuo4xdAhPOOGEBCE85ZRTFMmDnAdEjqFVWKJECWUFxO+0hVBbCd1IKvFrbcx4PyTOmG7snTt3qtAFhDCABEKLMYyN1vkwrkpwYyIBDA77WPRMAhiLZYzlJB5++GFF4ObNm6dICjIaJ06cqORokmVdgvAhcP/FF19URAXZiIhBnDlzZizxcWNSIISQ8dGEEHGbxx13nCLcqOsK7GrUqKG6sloIYUlDAxnUHxDvXLOM3ZiX9RrajQ0Siz1hurExT8S5QuIFoQ9xcHF7gSGvGT4ESADDtyaRGhEJYKSWK68GixjC22+/XXr16qXmjYc3dNegSdixY8cDsNB1i6E9qF13+FnXJq1cuXJeYZftZBHLiaxvJJKACIIQQuMOMYRNmjSRmjVrJjQMkxFCkD9TdiYMhFBXKsFYYPkza/YiWQnl+B566CEl9hxG8prtWvK8+CNAAhj/NfZ0hiSAnsLLi2eJACxQcMNB9+7ss89OXAVWGpTkgraZ2SA50759e0FGpdkQ4wVJm9atW2c5kvw5DZbWNm3aKEsrKl2ADIE8vffeeyp+EOLUsL7CTayFqatXr34AIcTx+MA6iE/QhFDL1lgrlcDy+fTTT6tYP2S8n3zyyfmz0JxpbBAgAYzNUgYzERLAYHBnr+kRWL9+vSop9fHHHwtIhm4geYhJQ3ya2VB3FDFcqERitgoVKiiLIXXJMu84xFDCHYxqI6kaSB1KAoIQohoGyn2deuqpiRhCuOq1hU3HDZpZxiCEZpaxVxZC9I24PnwQLmBWKsFLwi233KLiIVHfGsLabEQgigiQAEZx1UI0ZhLAEC0Gh5JAgBbAaGwGVH2B1Iy2EH711VdKL1LLzsCNrN2qJiHUpFATQk0K3SCEIHY7duxQBM8qWwOJnOuuu05ZixFeYLqDo4E4R0kE9iNAAsjdkBMCJIA5wceTPUQgWQxgxYoVZeTIkUljAEE23n///UQMIH4GGYGeIWMAPVwo49KoBAG5Ek0IYZGFGLWOIUS9aC8JIdzP0KNEHCLIn9nXggULpF+/fiqxCMLljPfzZ0+wF+8QIAH0Dtu8uDIJYF4scyQniTg/PKwh6wIyOGTIEIGrFzImybKAEcSPmK/JkyerjNWrr75aihcvLjNmzIjk/KM+aF09BIklOst4y5YtgnJ1mhAiqccuIYSVMJ3FDtZIWP6SxfshDAB1apEhDh1ENiIQBwRIAOOwigHOgQQwQPDZdUYEBg0apIL1f/rpJznjjDMSOoBwNdaqVUvmz58vjRo1UtdBDBsyhufMmaNIBQghCCRiBtmCR0BLsSxZsiRhIdy6das0aNAg4TIuKChwTAg10QT5R5avGe+HUIJu3bqpeskQeEZSEBsRiAsCJIBxWcmA5kECGBDw7JYI5DkCIG7bt2+XxYsXKwvh22+/rdy3IIS6Ukm5cuUOIIRmQgncvaYbF1Zhs2bvJ598IiifduONN0r37t19dflCggilEpFBDXHpN954Q7mdMzUnwueZrsW/xx8BEsD4r7GnMyQB9BReXjzGCDh5WDdt2lRJ2sA9CeID4jJ8+HBlnWLbhwBwgcUORBCSMwsXLhS4dc855xxFCBs3bqwseZr0IdYQcZ+w+qHBmgi5HxyPWFFUzRg/fnzCQuwnziCfILb169dXMZCvv/56RgLoVPjcz/mwr3AiQAIYznWJzKhIACOzVBxoiBBw+rBu1qyZIjHQ2GOzhwAIIdz6kJtBUgkIIX4HmRrEAj733HMq3vOCCy5QpBAJKAgJmD59uiLbmzdvVhJCIN/AH5ViksWO2htN9kdhrHYsgE6Ez7MfDc+MEwIkgHFazQDmQgIYAOjsMvIIOH1Yg4Ag8QGJLGzZIQDyh9hPVIFBhjeSSUAQYRkEtogJRVUPlK5DAhHcybAm6oxkZAGXLVs2u85zOMsOAXQqe5TDcHhqjBAgAYzRYgYxFRLAIFBnn1FGIJuHNQjg6tWrlTYdEh0KCwtV3VnUpWWzhwDIH1y8qBCDbF7guGnTJuUuBsl76aWXlIUViUBeSbx07txZJkyYoK4PQmptsDYintFsdgigU+Fze4jxqLgjQAIY9xX2eH4kgB4DzMvHDoFsHtaQQoFlCuQF9XUhRgz3JMqQsdlDAJngTzzxhKr4Ymb66rN1bKW9q2V3FKqIwNWcqmFc1soidghgNi8V2c2AZ8UJARLAOK1mAHMhAQwAdHZ5EAKw5EC0+c4771RlutAeeughGTNmjKxcuVJKly4dGtTceFjDNdm8eXMlb4PEELb4ImCHAGL2ToTP44sWZ+YEARJAJ2jx2IMQIAHkpggLAgjcR0A/MiYh6HvppZeqBAAQw7C1XB/WmgCCTFKbLmyr6854UIcYVkkknsybN08lo+iSd8l6cCp87s4oeZUoI0ACGOXVC8HYSQBDsAgcQgKBUaNGyYMPPijQexs6dKhcf/31oUTHycMa1k3owSFRAWTgww8/lE6dOkm1atVUxipb/BBA+UGzDrKeIaSDBgwYoP5bp04dueaaa5ReoG6phM/jhxBn5AYCJIBuoJjH1yABzOPFD+HUkblZtWpVFUf1+eefpy39FfTw7VYp+fLLL+Wqq66SNWvWCMSLK1SoIFdeeSWTQIJeQPZPBCKOAAlgxBcw6OGTAAa9AuzfRKBt27ZK2gPVE+AOht4eGxEgAkSACByMAAkgd0VOCJAA5gQfT3YRgZEjR8pjjz2m3KVbtmxROm/PP/+8XH755S72Ev9LQSIFrnRo5aHUGmrkIhEhVQPh7tmzp8ydO1cdB8Fk1FAuWbJk/MHiDIlAhBEgAYzw4oVh6CSAYVgFjgFls1q1aqX03E4//XQFyCuvvCJdu3aV5cuXy4knnkiQbCKAJJrvv/9eIFmCGMpMBBCED8eAOCJpoV27dkqfcObMmTZ75GFEgAgEgQAJYBCox6hPEsAYLSanQgQMBJBBff7556clgIhPRDLKBx98oJIS0PBzvXr1BH+rXLkyMSUCRCCkCJAAhnRhojIsEsCorBTHSQScIWCHAM6aNUvat2+vrIVmgzTNyy+/rCpvsBEBIhBOBEgAw7kukRkVCWBklooDJQKOELBDACdNmqQqa2zYsOGAayNTecSIEdKhQwdHffJgIkAE/EOABNA/rGPZEwlgLJeVkyICSkQ7kwuYFkBuFCIQXQRIAKO7dqEYOQlgKJaBgyACriNghwAizg+CxcgY1jGA+BnVVyBmzBhA15eFFyQCriFAAugalPl5IRLA/Fx3zjq+COzZs0clfoAAIrMa9YaLFCkihx9+uBxyyCEHTfySSy5Rx0+ePFllAV999dVSvHhxmTFjRnxB+mNmSHhBJQ5ID0F78o033lBW03Rt8ODBcv/996uqLsALmAJD4MdGBPxEgATQT7Rj2BcJYAwXlVPKawQmTJggnTt3TpA9TVIgsQNrX61atWT+/PnSqFEjhRN0AHv16iVz5sxJkBnoAJYoUSL2OH7yyScCCaL69evLmWeeqepQ2yGACxYsENRzZiMCQSJAAhgk+jHomwQwBovIKRABIpAzAhDBtmsBJAHMGW5ewAUESABdADGfL0ECmM+rz7kTAf8RcFqppGnTprJ06VI54ogjEi7X4cOHS7du3VwdvBMC+MgjjygXMD4NGzaUYcOGKT1FNiLgJwIkgH6iHcO+SABjuKicEhEIMQJOK5U0a9ZMzjvvPEHsnd0GFzhc4YjPgwvc2kAq//Wvfx3wa7sE8KOPPpKjjjpKqlSpouRzIKOzbNkyJaANQshGBPxCgATQL6Rj2g8JYEwXltMiAiFHwE6WMqYAAnjuuefKkCFDbM8Iwta//vpryuP/9Kc/KRJnNrsE0HrR3377TdVNnj17tjRv3tz2GHkgEcgVARLAXBHM8/NJAPN8A3D6RCAgBJwQwNWrVwuymwsKCqSwsFD69++v6hW72XIlgNBUbNGihZtD4rWIQFoESAC5QXJCgAQwJ/h4MhEgAlkiYJcAwr1ao0YNKVWqlKxatUquu+46qV69ukydOjXLng88befOncpNDPftvHnzBO7hww47TEnnJGvTp09XmcJly5ZV0jFwAS9atEiNzW1S6soEeZHYIkACGNul9WdiJID+4MxeiAAROBABuwTQihvkV+Bqhb4hEkNyaRC7hjSOVR9x4MCBMmDAAHVpCGRfc801Si8QDRZIkNKff/5ZSpcureIToQt4/PHH5zIUnksEHCNAAugYMp5gIkACyP1ABIhAEAjkSgC3bdsmRYsWDWLo7JMIhAIBEsBQLAMHQQSIABEgAjYROFRE/iQiTURknoggG2O3iPwmItaU3fIiUl9EForILyJSW0TGi8haEbnKZn88jAgQgRgicHBdoxhOklMiAkSACMQIgetEZJxB9nAfB/FrJiJfiMhHItJSRBaLSFURmS4iJ4sIgvK+FZF/iMhQEfk5RphwKkSACDhEgATQIWA8nAgQASJABIgAESACUUeABDDqK8jxEwEiQASIABEgAkTAIQIkgA4B4+FEgAgQASJABIgAEYg6AiSAUV9Bjp8IEAEiQASIABEgAg4RIAF0CBgPJwJEgAgQASJABIhA1BEgAYz6CnL8RIAIEAEiQASIABFwiAAJoEPAeDgRIAJEgAgQASJABKKOAAlg1FeQ4ycCRIAIEAEiQASIgEMESAAdAsbDiQARIAJEgAgQASIQdQRIAKO+ghw/ESACRIAIEAEiQAQcIkAC6BAwHk4EiAARIAJEgAgQgagjQAIY9RXk+IkAESACRIAIEAEi4BABEkCHgPFwIkAEiAARIAJEgAhEHQESwKivIMdPBIgAESACRIAIEAGHCJAAOgSMhxMBIkAEiAARIAJEIOoIkABGfQU5fiJABIgAESACRIAIOESABNAhYDycCBABIkAEiAARIAJRR4AEMOoryPETASJABIgAESACRMAhAiSADgHj4USACBABIkAEiAARiDoCJIBRX0GOnwgQASJABIgAESACDhEgAXQIGA8nAkSApC8yVAAAAglJREFUCBABIkAEiEDUESABjPoKcvxEgAgQASJABIgAEXCIAAmgQ8B4OBEgAkSACBABIkAEoo4ACWDUV5DjJwJEgAgQASJABIiAQwRIAB0CxsOJABEgAkSACBABIhB1BEgAo76CHD8RIAJEgAgQASJABBwiQALoEDAeTgSIABEgAkSACBCBqCNAAhj1FeT4iQARIAJEgAgQASLgEAESQIeA8XAiQASIABEgAkSACEQdARLAqK8gx08EiAARIAJEgAgQAYcIkAA6BIyHEwEiQASIABEgAkQg6giQAEZ9BTl+IkAEiAARIAJEgAg4RIAE0CFgPJwIEAEiQASIABEgAlFHgAQw6ivI8RMBIkAEiAARIAJEwCECJIAOAePhRIAIEAEiQASIABGIOgIkgFFfQY6fCBABIkAEiAARIAIOESABdAgYDycCRIAIEAEiQASIQNQRIAGM+gpy/ESACBABIkAEiAARcIgACaBDwHg4ESACRIAIEAEiQASijgAJYNRXkOMnAkSACBABIkAEiIBDBEgAHQLGw4kAESACRIAIEAEiEHUESACjvoIcPxEgAkSACBABIkAEHCJAAugQMB5OBIgAESACRIAIEIGoI0ACGPUV5PiJABEgAkSACBABIuAQARJAh4DxcCJABIgAESACRIAIRB0BEsCoryDHTwSIABEgAkSACBABhwj8f21knktHm7b6AAAAAElFTkSuQmCC\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.Axes3DSubplot at 0x1248f5bd0>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_mug_3D()\n",
"plot_pail_3D()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dimensional reduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Many dimensional reduction methods will obscure the topological nature of your data. Here is an example of where PCA does a good job of keeping some of the character of a coffee mug, but doesn't do well with a bucket."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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"<IPython.core.display.Javascript object>"
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%run pca_demo.py"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/dylan/anaconda/envs/gp27/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
"/Users/dylan/Documents/programming/me/python/tda/Dionysus/build/bindings/python/dionysus/__init__.py:1: RuntimeWarning: to-Python converter for boost::shared_ptr<PersistenceDiagram<boost::python::api::object> > already registered; second conversion method ignored.\n",
" from _dionysus import *\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Distances Pairwise: time 3.19480895996e-05s\n",
"Distances Explicit: time 0.00259494781494s\n",
"Rips generate: time 0.0278868675232s\n",
"Filtration sort: time 0.148832082748s\n",
"Static persistence pairing simplices: time 0.00386214256287s\n",
"Dynamic persistence pairing simplices: time 0.0202660560608s\n",
"Simplex mapping: time 5.00679016113e-06s\n",
"[<31>, <0>]\n",
"[<32>, <0>]\n"
]
},
{
"data": {
"image/png": 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0bdo0j8Mrq1atUpUqVfTiiy+WWhO3UaNGqVy5curQoYMiIiLUpk0bXXHFVQoK\nGlLIM5vj/nocI9Ms9FtBmGDLceXCw1vqk08+8fepnfMMHPiQ4KVC/o+/ChoKsouhBL5Uixbt5XK5\n9NZbbyk2NlbDhg1TRkZGsWRwuVyqW7myfgDtBc0CpYByQQtATsyJXoGmgNpiTgx/jTk0lKcECptA\nllsxRLh//wiqHRd32gYRpQlLCZQisrOzNXHiJJ1//oUKC6slw3hWponnB4Lxcjrby+ksrwceeEQ7\nd+48etzcuXMVGxurDz/80I/SF81rr72matWqaevWrZKk1NRUzZo1S0FBTsHWQl4I62SGlcyf114w\nrEDedCUktPPz2Vk899xzCgp6sJD/4xiBUxAnqOT+bRc0LaTsFF1zza1H69yxY4e6dOmiBg0aaNmy\nZcWSY+yYMbrV4dA+UGtQtHuYpwno7XyN2UAB+RIgO54nkAV6FXSp+7cL1Dg8XN9//72vLmmJYSmB\nUkJycrISEzspLOxywQJ5XjG7WSEh9ysmprJWrlyp0aNHKz4+Xj/99JO/T8EjY8eOVfXq1fXXX38d\nl79y5UpFRDT2cJ6FKYF2gusL5B1RcLDdPydmcZSVK1e6jQ8KzuukC/bkSw8LblRhC8mczqs1derU\n4+p1uVyaNWuWKlWqpMGDByslJaVIOQ4ePKjy4eFaXPhNdTTVwlw7kD/P0wSyQL9hzg0szZd3S3i4\npk+f7svLWiJYSqAUkJmZqYSENrLZ7hBkFXXv5kuzFRwcoVq1aunvv/8uUXkzMjK0bt06LVmyRCtW\nrNA///zjsezo0aNVo0aNQmVctGiRIiMTPZxftuB8mWPJ2YKvBCGCqwuUcykgIMjjHIhFydGwYYLg\ns5Pct56Gg/6Q0xnrcbhz37596tmzp2rWrKlFixYVKceiRYtUweHQsiIEiQP942FfwQnkLZimpDMK\nlLvL4dCkSZN8cSlLFEsJlAIGDhxyGtYxEkxRpUo1S2xi9M8//9SDDz6q8PDyCg+vq8jIBEVGXiyb\nLVYXXXSFZs2addzLeNSoUapZs6a2bdtWaH0//PCDIiNbFnF+awWtBbHul38vQZ8CZXJkGAFnxdhs\nWWfatGkKC0tU8fw+HZ+Cgwdo0KAhJ21j3rx5qlq1qvr06aODBw8WWS7W4dBLAQH6r5AG64PWeRAm\n/wTy35iLyCYVUq5beLg++ODUzalLG5YS8DPJycmy26MF2wWvCy4RhOp4R29Zgm6C6jLdP3x3dF94\n+GX6+OM0afp/AAAgAElEQVSPfSpjVlaWevXqK5utnEJCHpS58lMF5PtQ4eFXKioqTosXL9bIkSNV\nq1atIlf3btmyRXZ73Ckov5YyTVvz5/2hiIiKPj1/i+KRmZmpiy66TCEhD8mczC+eAjCMKapQobp2\n795drHYOHz6sfv36qXLlypo7d67HcmvXrlWvG25QZGiobrfb9QFoIehzUDNM1xFFTSDvBJ0PeqUQ\noTNBlex2/f777966fH7DUgJ+5s03Jyos7Fr3vfWJYK6gfyFKYKxgqSD+OCUAM9W8eVufyZeZmanL\nL79advs18rzQLH/6RsHB0YqLiyuWv5W6dS8RfOGhrjUyHdulyhwWqqmCw2V5Po4sSgf79+9XrVoX\nKDS0nzwvHMtLuQoMfFXR0fGn9TJNSkpSrVq1dPPNN2vPnj0ey+3du1cvPf+8rr/qKrW5+GJ1atVK\nndq0UR2bTXvxPIH8FOakcbg7OUFhoPcwzUXrVqumr7/+usybKFtKwM/UrdtM5kRw/oejoMvn/KlK\nASWQIbu9wlGrG29z4423yW6/VsdM/IqTfpHNVk7Lly8/af3vvPOOnM7OHuoZIoiWOUHcSSdaEWUo\nICBSs2bN8sm5W5wehw8fVvv218lur6igoMeV55TwWNovwxilgIDKatCg+RnNaaWmpmrIkCGqUKGC\npk+fXmzzaJfLpfpVq+rbYtzQmaD3QZdjOpy71a0EeoaGqnl4uKrExOiZESO0a9eu0z4Pf2IpAT/j\ncEQL/jsDJSBFRrbRwoULvS7bihUr3BYfBwV3CaoJIgQXCebnkyFNcK/MsfsomeP4U9SsWZuTtpGa\nmiqnM1ZmL6e4SsZMAQEvq2bNC1S1alV16dJFa9eu9fo1sDh9NmzYoH79BsnhiJHTWUMRERfI6ayl\n0NAIXX99T1WpUkWLFy/2SlsrVqxQ48aN1blz52I7GJw+bZpqu81JPd1kf4EagBIxF45lFVJmFehu\nm00xdrs+nD3bK+dTklhKwM8EBgbLHPI4fSUQEdFFc+bM8bpst97aWwEBL8gcjnlK5ryFBPPcX+d5\nX3g9BLfKNPtzCVYJMmW3VyxWN/+LL75w+51ZdwpKYLaiouL0559/Kj09Xa+88ooqVKignj17+qxX\nZHF6pKen648//tCqVau0adOmo+5D3nnnHbVt672hzMzMTD311FOKjY3Vm2++eVJjgaysLDWp11B1\nQP8WcpP9iWkVNKaYN+UqUGW7XVOnTPHaOZUElhLwMw5HjMzgLqevBCIjr9Q333zjVbkOHDggmy1K\npm13YXI0kRmMZqNMtw4nzhcEBT2he+4ZWKz23ntvhuz28oLZKnro6YgCA59TdHS8Vq1adVwdhw8f\n1vDhwxUTE6MBAwaU2e75uUJWVpaqVaumH3/80av1rlu3Ti1atFDr1q2P+qcqrO3WrTvJZuusIIYq\nErueJkC73TdaGqgex5uKFif9Dqpgt2vJkiVePSdfYikBP9Ow4aU60bb6VJRAmmy2WI9mmKfL559/\nroiIdh5k2K1jYSinCRoLBsscDmoi+NhdbrUqV65X7DaTkpLUqNGlcjiqut1Jb5A5VLZbsOKoG+kO\nHa4/YeFZfvbu3avBgwcrJiZGQ4cOLdKU0MK/jB8/Xp07d/Z6vTk5ORo9erTKlSunl19++YTJ2549\n75bDcU2+D46VstFdodh0NWHqgKE27hveU4Sy30GXYE4qx2A6m/sd0yVFx8su8/o5+QpLCfiZKVOm\nyOns6L4RPYWBlExLi3S3Evhax4aQpujyyzt6Xa5p06bJ6exRiALIFlwlcw5Agudlmq0+7d73nUzX\nABsFO0/LfHPVqlXq0aOPKlSoKYcjWk5neVWpUl9Dh/6vyIVpBdm2bZt69+6t2NhYvfjii0pNTT1l\nWSx8S3p6uuLj430WrGXr1q1q06aNmjVrdtRD7qZNm2S3Vyi09wp/ypzzQhVBM/Ecoeywe8hImKuN\nx2FaF6WBYm02/fHHHz45J29jKQE/k5aWprCwcoI/VHQYyOoF8gME2xQe3kzz5s3zulzTp09XePgt\nBR4Ql0yf8J3zKadXZa5ryG8X/n+CcYLtioqK97psp8qGDRvUrVs3xcfHa8KECWcUuMTC+7z66qu6\n/vrrfVZ/QYd099wzUMHBj3no5d4iaK9Y7PrO/fX/e74CnhzMZYNexzQhFejh4GA9PLB4Q6H+xlIC\npYDHHhsmh6OtTm5XfXwKCHhd1as3VE5Ojtdl+uqrrxQRcVmBNu8UFJRzkVsJ5F8l2sWtBH5U9epN\nvC7b6bJixQq1a9dO559/vmbMmGGtMi4lpKSkqEKFCkdjTPuKHTt2qFOnTjIMh+CvQp6pVJmuSUao\nJ3YJdBtoKEVHKIsCBYMCQc+78xaBWl94oU/Px1tYSqAUkJOTo6uu6iK7/XoVL7KWZBhvH7WO8QVp\naWlu080/3G3eI7jU/aDklyVbpq//Z929gyUyzUg3KTS0n558coRP5DsTFi1apObNm6tJkyaaN29e\nqXW9fS7xwgsvqHv37j5vZ9myZbLbPTkt/FWmu/Ln9TABEuZq4S75hnw8OZhLA00AfeHeXgm6oEYN\nn5+PN7CUQCkhIyNDXbveorCwiwSziugV/CKb7TZVqlRTmzZt8qlMAwc+rODgh2WaghoyJ4Od7hQu\nmOmW6Xe3gnDK9B0/V3BYoaFRx7m8Lk24XC598sknatCggVq1anVWuAQuyxw+fFixsbEerXm8xfz5\n8xUZ6cng4QeZLq9Hqx8hEugt0JUFChZ0MJeXXKByoH1uZZFQv75Pz8VbnIkSCMDCa4SGhjJnzgze\nffdxLrlkIqGhVYHBwBvAJOAFwsNbEBt7A08+WZ/161dQp04dn8o0aNC9BAa+C4QALiANSHanI8Ct\n7pL1gR/d+euALgQEvMaVV7YlPj7epzKeLoZhcO2117JmzRr69u1Lr1696NSpE6tXr/a3aOckERER\n3Hfffbzwwgs+bSc4OBjI8bDXiXlfx7OWUAAOA+EFSuUAWws5OhfzCdkJbAHiqlTxgsSlG8NUIqUH\nwzBU2mQ6XYYMGUJS0vc0atSUzMwsKlaMpk2bK+jUqROBgYElJsewYc/wyiufkpb2LRBZzKM+Jjp6\nIKtWLaV69eo+lM57ZGZmMmnSJJ5//nkSExN5+umnqV27tr/FOqc4cOAAtWvXZuXKlT67b9auXcul\nl15LauofgFFgbxoQA6zARgKbSONhIAuYAdiBhUA3YCSwHMgAIgAH8Cfwq/tvm/BwHpo6leuuu84n\n5+FNDMNAUsGLUTxOtwuRPwFXAxuBzcCjHsokYl7fdcDiIuryfl/JT3Tt2rVU+MRxuVzq2/d+BQSc\n7x72KawbnZcyFRDwqiIjK/nM5M/XJCcn69lnn1W5cuXUt2/fYjnBs/Aejz32mPr16+ez+l0ul6pW\nrS9I8nAP3yrormB661YCFYHpcTTPwVw1zEVkFUHXuP/aMK2CojAthwaC4qOiyoxjOfw5JwAEAH8A\n1YBgYDVQr0CZSGA9UNm9HVtEfT67UCWJy+VSbGzsKdnE+5KhQ4eqbt0GCg+vIKfzKpkrhfPmLFyC\nbQoK+p/s9kpq2rR1mbGPLor//vtPQ4YMUUxMjIYMGaL//vvP3yKdE+zdu1fR0dE+Vb7jxr0mh+Nm\nD0rggOBagUMGxtGx/0xQD9AFmM7kMj18Ca0A3Qyq4HRq48aNPjsHb+JvJZAAzM+3/VjB3gBwL/B0\nMevzyUUqaTZu3Khq1ar5WwxJpqlo5cqVtWfPHmVkZGj69Olq0qSVAgICFRwcpoCAYDkc0brrrgE+\nN/HzBzt27NA999yjcuXK6ZlnnlFycnKxjz148KBGjx6jiy5K1HnnNVKVKg3UuPFleuqpZ4vtO/9c\nZPDgwXrggQd8Vv+hQ4fcMTx+K7JnG8BoVcWh7aDrQV0pPPZwYeltw1BcVFSZ8GPlbyVwAzAp33ZP\nYFyBMq8CrwOLgRVAryLq89V1KlEmT56sHj16+FsM7dy5U5UqVSrU06PL5dKRI0eUkZFR8oL5gc2b\nN+uWW25RpUqVNG7cuCLPe+fOnerRo49stig5HN0FX7pfOGsE38hmu1s2W5T+7/9u8bmFV1lk586d\nio6OLjJGwJkyc+b7sturylwh7Pl9HsjTiiRIl4MyiqkA8tLrAQFqUK2aT9bxeJOyoARewzQ9sQHl\n3HMHtTzUp+HDhx9N3nJTW9LceeedmjBhgl9lyMnJUWJiop566im/ylHa+PXXX9WpUydVr15dU6dO\nPeEBX79+vWJjz1NQ0CMyfR55ekcclGG8qPDwivrhhx/8dDall3vvvVePPvqoT9t47bXx7sh2X8lz\nJLR02bBpM559COVPT2EuKFvk3m7udOqzzz7z6XmcKosXLz7uPelvJZAALMi3Xdhw0KPA8Hzbk4Eb\nPNTnm6tWwtSuXfuonxN/MWzYMLVp06bUf8X4i++//16tWrVSw4YN9cknn8jlcmnbtm2Kiaksw5h6\nCh+MCxQWVl6rV6/29ymVKv7++2/FxMRo//79Pm3n888/13nnNZDTWV+G8Zpgh0y/XMmC9YIOaun+\nZ3nyIZT3z9wKaozpfjpPCUwFXd2qlU/P4UzxtxIIzDcxHOKeGK5foEw9TMusQExLrLVAAw/1+fBS\nlQy7d+9WVFSUX10aLFy4UHFxcZYr5pPgcrk0b948NWnSRC1atFDduk0VGPhSIS/6v2VGRouWuRjp\nPh3vZmOmqlSpY7mxKEDv3r01fPhwn7fjcrn03XffqXPnmxQeXl4BAUEKDrYrNraaogOD9Gkh2rsw\nH0JXg+ZjBqbPUwLpmM7kivJ662/8qgTM9rka2IS5vuIxd949QN98ZR52WwitAe4voi6fXaiS4uOP\nP1anTp381v6uXbsUFxfn9fgEZzO5ubkaOXKkDCNaBWMgm6mT4A73vj0yXW+/lm+/S07nhfrqq6/8\nfSqlis2bNysmJkbvvPOOLr44UQ5HjAwjQKGh4TrvvIZ66aWXfW61FREcfDTGgPDsQ2g26Fr37/xK\nQKC2kZH6+uuvfSrnmXAmSsArK4YlLZBUV1JtSS+68yZKmpSvzChJDSU1kfSaN9otrSxdupRWrVr5\npe3c3Fx69OhBnz59aNu2rV9kKIsEBASwcePfBAbeh2npXJC/gZvd+ypgfvesz7ffICWlPy+99IbP\nZS0rSGLOnM9JTs6hX7/JrFrVn7S0TUgZZGb+w/btExkxYg2VK59Pjx53k5yc7BM5Ml2u41YMvwGk\nAEuA64FQ9/YTwDgPdURIHD582Cfy+RvLbYQPWLJkCZdddplf2n7uuefIzc1l+PDhfmm/rCKJmTOn\nk5PT10OJB4BZQDqmU4H5QMcCZbqzZEkSBw4c8KGkZYPc3Fy6d7+Lp5+eSXb2j2RlLQVuBGIxFWkk\n0Ir09GlkZm7h449F06ZXsGfPHq/L4gwNJbVAngG0BP4BxgMjgNuAqh7qSDYMIiIivC5baSDI3wKU\ndbZu3crbb09l48a/SU5OJTzcwapVq6lRo0aJy5KUlMSECRNYuXJlibqlOBtITk4mNzcX8OQr5nJg\nIqaDARdwO9ClQJkwQkPj2L17NzExMb4Ttgxw//0P89lnf5KW9h0QdpLS5cnMfIu//nqSK6+8hhUr\nkggLO9kxJyc7O5snhwwhNz2dlZh9t4LkYLqI+A7YgdlLANgH3IRp0TIIWJeVRc2aNc9YplLJ6Y4j\n+SpRRuYEFixYoMsuu1o2W6yCgx8UvCsztu5kGcbNCg2N1A039NKvv/7qlfaSk5O1du1aLV26VKtX\nr9a+ffuO279nzx5VrlxZCxYs8Ep75xp79uyRzRbrwfrHJagmeME9J3BA0FXwyAllIyIu1C+//OLv\n0/ErS5YskcNR3X2dCl7L1gKbTA+2TkG9466zzXajnnhi+BnLkJqaqnatWqmT3a6XMBeJ7QXNAqWA\nckELQE7QPNAB0J58qSroY/d8wQegxKZNz1gmX4K/J4a9mUq7EnC5XBoy5Ak5HDXcL35PsQP+U0DA\nSDkc5TV9+ozTbm/NmjW6445+stujFB5eX5GRLRQR0VihoZFq1+46LVy4UDk5OWrfvr2GDh3qxTM9\nt8jIyFBAQLAKtzX/T2YUuCP58j51Tw4fX9bprHFWuNw4E7p27S7DGOPhuUgUvONhnwRrFRUVf0aR\n43JyctS1XTv1sNmU7X7px4B+BbXmmA+hJqC3PQhSI9/EcGunU7Nnz/biFfI+lhIoQR56aKgcjksE\ne4u4kfOnNXI4KmvWrA9OqZ1Dhw7pyiuvkcNRWYGBTwl2Fqg3WfCmnM7GioqqrKZNm5YZZ1ellWrV\nGgoWe/g/ni8YKTPozkHBdYKeBcpsUnh4BWVmZvr7VPyG2aOKcl8jT0rg7SKfmfDwy/Xhhx+etgxT\np07VpWFhyuTY4rBQTAdxk/I19A2mI7kwUBvQtkKEmWEYql6xYqkPZ2opgRJi7ty5cjjOF+wrpgLI\nS6vlcMRqy5YtxWrnv//+U82ajRUaep8KN1fMn1xuZVDBa0NP5yqvvfa6wsJu9HCdf3O/wKIF5WXG\naT7+QyAoaKAefNC3K2RLOxMnTnS72fB0vyYKKriv4WUq3BPoFHXseONpy9CiQQN97q4sb3FYJuhS\nkB30E+g/d2/gY/e+IaCEAoJ8AirvdGrt2rVevEK+wVICJcTFF7eWOe7/uuASmXF578x33ywXtBPE\nuG/0mwS7BFJw8GO6774HT9pGZmamLrywlYKDh8jzMvjC0oeKialcaryWlkUOHz7sdkq24xSVvAQp\nMgynGjZsqAULFpyz4S6feeYZGcbjRVynnwUp7o+bqTLnBgr6/vlWF1zQ+rTa/+WXX1TN4TghdKQw\nw0WGur/+B4Fa5duX6lYQm0B/gAYGBSk+OlorVqzw7gXyEWeiBCwT0WKyfv16NmzYDFwLVAb+B9xV\noNRBzDVy29zJCdwJQHb2PUyZMpW0tLQi2/nwww/ZsiWQ7OyngbuB6pjmdBcDC9yltmFa90ZgxkyK\nADZx5Eh3nnlm5Jmd6DlMREQE999/Hw7HrZihRoqLC7v9Dm68sRsjRoxg0KBBtGnThuXLl/tK1FJL\nVlY2UlFGh80wrYWCMY0yWwFfFigTTHZ21mm1P3vGDG7PyCC/bdwAd4uXAA3d2zMwA5s8D8x0SxAD\n3IL5pK1o3Jif167lkksuOS05yhKWEigm48e/TVbWXZg377WY5oEFzQCvxvSn58T0lXcfpt88gOoY\nRgJz5swpsp2XXhpPaupgTDPE84AfMAPkPYNptLbdXdJw5+eFiXyCnJyBTJ8+g5SUlDM51XOaF14Y\nQdu28TgcXTCv68nIxGbrRcOG+5g27U26devGunXr6NmzJzfddBNdu3Zl3bp1vha71BATE01o6P5T\nOMIAVCBvP9HR0afV/t4dO6jmch2Xl39x2A1AP8yntyuwB/gcUxFkA02BF4F6tWtTuXLl05KhrGEp\ngWKybt0f5Oae6lfBd5jfHiYpKU3ZsuUPj6VXr17N1q3bgWswXSwN49jylc5ADWCle1uYiiI/VTCM\n1syYMfMU5bTIIyAggDlzpnPzzbVwOJoQEDAS+K+QkkeA8YSFXUTr1ul8//18QkPNmLZBQUHcdddd\nbN68mcTERNq2bUuvXr34888/S/JU/ELr1q0JDPwcM1pvQQ4DXwOZ7v0zMD9yjrfgt9s/pXPnK06r\n/eysrEIXP+VfHDYBs/8cDowF3gfmYK4D74TZa8jOOr2eSFnEUgLFJDk5BfMLv7iswfx6H5Uvz8nB\ng56Xxn/zzTdkZ19P4Wv49mB64G7k3jYwh4rOA3oD5tdXauqNfPTRV6cgp0VBgoKCeOed8Xz77Qfc\ncMPvhIbWwum8ntDQ+wgJGYjTeQs2W3U6dPiWzz57nfnzP8Zut59Qj81mY/DgwWzZsoVatWrRvHlz\nBgwYwK5du/xwViXDRRddRI0a8Zw4xAPmt/aTmK/b8pjf6HOBWvnK7Ef6lLvvLjjUWjyiK1SgqH5I\n3uKwRpieLvNIxQw83xBT5UdXqHBa7ZdFLCVQTCIiwjGHXorDH5jfFK9hfn+YGEYyMTGel57v23eA\n7OzCbr4czDANdwK1MZfer8CcG1jplquHu2wF9u8/WEw5LYqiRYsWzJ79Ljt3bmXixG6MHFmHl16q\nyRtvdOKPP9ayYMFHtGnTBsMoOr53REQEw4cPZ8OGDYSGhtKoUSMef/xxDh06VEJnUrI8+mh/wsLG\ncuIwTyzwM2aP4ADmUGmb40oEBLxF587/R2xs7Gm13bp9ez4NNz0F7QM+wHzBu4CvMB1/XIU5oLse\n+ASzX/IUcCFQB/gkPJwr2rU7rfbLJKc7o+yrRCm1Dho0aIiCgwuuEH2ygHWQZLocri6YdIJlRHh4\nO73//vse2xg69AnBU4WYgN4s6CzTRr0wi4vdAsNtdfGVLrmkbQleGYtTZfv27brrrrsUGxurF154\nQampqf4WyatkZGSoXr2mCg4ueC+fLC2W01leGzZsOO22s7KyFB8VpbWgfRS9OGyR21LIAboSc53A\nb5gB5kv7uoCCYFkH+Z7+/fsQGDgF02okN9/fHI6Ncf4LtAXux7Tsyc9m4Deuu+46j21UqBBLaOiO\nArl3YXZQ5wBF+QMyML93dlKx4ul9RVmUDFWrVmXy5MksWbKEVatWUatWLSZMmEDWWTIOHRoayuLF\n8yhf/j2Cg5/kxB5BYXyBw3Ejc+fOol69eqfddnBwMHcPGMC40FBigSTMPsch4DfMgdM82gAbMHsK\n32IOrI4LDaXvffcRHFyYJ9mzlNPVHr5KlNKegCQlJLQTvCcY4f7yDsiXnnKnALftc55vlHCBFBLy\ngB566LEi69+6datstnKCVPfHyj2CS/Nt56WfBJvcvYT/3D2Ftu7eRqI++ODUVidb+JdffvlF7du3\nV82aNTV9+vSzJjDN3r17ddFFl7kjfo0THCpwH2cLPpHT2U7R0fFavny519qtVr68phnGqXRDNNUw\nVK18ee3du9crcpQkWIvFSoavvvpKDkdVnfpioh8VEODUjz/+eNI2rriik0zfKtvcisbuViZ5CmWm\n4H1BDXdevOB2mYFO1isystI57bagLLN48WIlJCSocePG+uyzz86KBWd5Eb+uueZmhYZGKTLyckVE\nXKOICHP1daNGl2rGjBnKyMjwarvr169XpchIvRkQUKyH9M2AAFWKjNT69eu9KkdJYSmBEuTpp1+Q\nw9FAsL2YCmC57PaKuvPOO1WxYkV9+eWXRdY/b948hYU1kmfHdJ6SS6GhPfXoo0+W0JWw8AUul0uf\nfvqpGjZsqJYtWyopKcnfInmN3bt3KykpSXPnztXChQtVv359LV261GftbdmyRfXOO0/Nw8P1Liit\nwEOTBpoCahYervrVqpVpx3+WEihBXC6XXnxxlByOym5PiZ4cZW1TUNATcjhiNW/ePElmYPMqVapo\n6NChHp295ebmqmvXW2S336CT+w06lgIDX1DNmo116NChkrwcFj4iJydH06ZNU40aNdShQwetXLny\ntOpYvHixpk2bpjfffFPvv//+GU26epv+/ftr1KhRPm0jJydHn3/+uTpefrlibTZdERmpzpGRuiIy\nUuVsNnW8/HLNmzdPOTk5PpXD11hKwA8sW7ZMXbrcKpstSjZbb8EYwVuCVxQW1lUOR4zuuWegNm/e\nfNxxe/bsUbt27XTFFVdo586dhdadnp6uxMROcjg6KM/3kOeUopCQB1WlSl3Lb9BZSGZmpl5//XXF\nxcXppptu0qZNm056zN69e/Xssy8oNraawsMvktPZQ3Z7H4WH3yi7vZKaNk3U7Nmz/W4B895776lb\nt24l1t62bdv07bffau7cufr222+1bdu2Emvb11hKwI/s3r1bo0a9or5979fNN/dW//4PaPLkyUpJ\nSfF4TE5Ojp5++mlVqlRJCxcuLLRMdna27rvvYdlsUQoOvl7wnSD36NAP/K6QkIGy2WLUocP1Pg/W\nbeFfUlJS9Pzzz6tcuXLq06ePR4X/6aefyuGIkd1+p0xnbQU/GjIFs+R0Xq4aNRr59UX4xx9/qHLl\nyn5r/2zCUgJllEWLFikuLk4jRozw2B09ePCgqlWrodjYGjKMQIWERCogIEiRkZU0ZMjj+vvvv0tY\nagt/sn//fj366KOKiYnRQw89dFyEuenTZ8pujxOsKNYcUmDgaJUrV8Vv95DL5VL58uW1fft2v7R/\nNnEmSsAwjy89GIah0ibTmZKVlcXGjRs5ePAgQUFBlC9fntq1a2MYBrt27aJ79+4EBQUxffp0Klas\neNyx//zzDxdeeCG7du0iICCA5ORkwsLCCAkJ8dPZWJQG/v33X5599llmz57NwIEDadWqFV26dCct\n7Vvy+6s6GYGBY6lSZSIbNqws1PWFr+natSs9evTgpptuKvG2zyYMw0BS0UvXPWAtFvMhO3bsYOjQ\nYVSoUI3LLruZrl2fpHPnIVx0UVtq1ryACRPexOl0snDhQhISEmjatCnff//9cXV8/PHHdOnShZCQ\nEIKCgoiOjrYUgAXx8fGMHz+en376iU2bNtGx442kpT3HiQogz9V4ntvxIMzQ6Sa5uYPYv78aH3zw\nQUmJfhyXXnopy5Yt80vbFm5OtwuRP2G6AdyIuSz20SLKNcP0InV9EWW831cqYXJzczVw4BDZbNHu\n6GDrCnTFcwULFRZ2nRyOaM2cabqSmD9/vipWrKjnn3/+6IKhli1bntSs1OLc5q+//lJISIxOXFRY\nMKXIXGuypED+PNWr18wvsiclJalFixZ+aftsAn/OCWD2Jv4AqmE6218N1PNQbhEw72xWAqaJ561y\nOK4Q7C/G2OxqORxVNXbs65JMvzItW7ZUx44dtWbNGsXExFiLvyyK5KGHHlNIyIPFuNfelRkruWB+\njhyO6n6JopWSkiKHw6H09PQSb/ts4kyUgDeGg5oDWyRtk5SN6aivayHl7gc+AvZ6oc1Sy+DBj7Fw\n4aK4SpoAACAASURBVL+kpX3FiUFnCuMC0tK+57HHnmfu3LlUrVqVpKQkGjVqxBVXXEFCQoI1/GNR\nJN9++xNZWR2LUXIaZjSvggTicnXgp59+8rJkJycsLIy6deuyatWqEm/bwsQbSqAyZqyGPHa4845i\nGEY8cK2kCZiezs5Ktm/fzqRJb5OWNgszqlh1TgwN+RPQHigHVARuBmykp0+nf/8huFwugoODGTly\nJJUqVWLp0qWMHj06r5dkYXEChw8fAk4WiWsb8D1we6F7s7Ji/Oba2poX8C9FBQP1JmOAR/NtF6kI\nRowYcfR3YmIiiYmJPhHK27zxxiRcrp6Yk3B5oSGrAl9ghoZcx7E4xB0wL/8AzDgBX3LkiJ1FixbR\nrl07duzYwZ49e/j555/p2bMn33//PVOmTDntsHsWZy9mRLOTxUR+D7gMc9T2RAID07HZynlZspOz\nZcsWtmzZxuTJs3j88eFILsLComnfvj0PP9yfZs2albhMZYGkpCSSkpK8U9npjiPlJSABWJBv+zEK\nTA5jBvP5E/gLMwLKbqCLh/p8MmbmazIzMxURUVGwwcN4bBPBnELyVwki3L/f1FVXdZUkjRkzRrff\nfvvRugcOHKgaNWr4ZdzWonTTrt31giknmQ+o454T8LQ/URdeeKGee+45LV68uMjFjt5gw4YNatmy\nvez2CgoKekSwXnDYPXn9pwICRiosrIbq1r1Eixcv9qksZwP4eWI4kGMTwyGYE8P1iyg/hbNwYnjV\nqlUKD2/o4QFbLwgWtBFcIWgn6CWYLxgt0120BP8pNDRcktSqVSt98cUXx7Xx0UcfqXz58nr99dfP\nCg+TFt5hzpw5Cg9vVcQLfqlMj7MpHvb/I5stSu+9954efPBBJSQkyOFwqGnTprrvvvs0c+ZM/f33\n316755YsWaLw8AoyjNcEGUXInSP4WHZ7Bb333gyvtH224lclYLbP1cAmYMv/t3fe8VFV2QP/3tSZ\nyaRC6Iggii5SFBBEkbKu0pvCKgILItXGqgj6Uxd0bUsVEVaaggUXUFesSJFFFCxAUFAQECKGGmpI\nn5nz++O+QAiZzCSZNHO/n8/95M2b+94972XePe/ec+45wARr30hgRD51F/4RlcCaNWskOrpDPp3/\nXQIhAo0ElgusFfhM4BWBK0XnH7hX9HJ+tygVLPv37/fqFbR7925p3ry59OvXT06fPl0GV2oob2Rn\nZ0tcXB2BBC+d6UjR4cbz72xDQp6UYcPuveCc6enp8tVXX8nkyZOlT58+Ur16dalVq5bcfvvtMm3a\nNNm0aVORvNa2b98uTme89QLkrfPPW7aL3e47Am9lpsyVQCBLRVUC69atk+jodrl+uJ8JVBU9DfQX\nuTg15G6B2qIT0dxqjRKOi1LBMn369HNTQfmRnp4uo0aNkoYNG0pCQkLpXaSh3DJx4j/Fbu+ez+/M\nV0kUu72abN++vcDzezwe2bt3r7zxxhsyZswYad68uTgcDrnhhhtk3Lhx8v7778vhw4d9ytmkyfWi\n1KteZFkicJVAhEBDuXA9wwaJiqpmXEm9YJRAOWDHjh0SEXGp6OBu/xOIF+gqOuNXZp4fe948xC7r\nba2NRERUkRtvvPFc+OmCePvtt6Vq1aoyd+5cr0P1Y8eOyfPPvyhXXtlaqlVrIFWq1JP69ZvLffc9\ndFGEU0PFJSMjQ1q16iBhYaPlfKBBX+WwOByN5cUXpxapzTNnzsjq1avl6aefls6dO0tMTIxcdtll\nMnDgQJk9e7YkJCRcEBNr8+bNVlKm7Hxk+dx6JnKC3h20yvk6Tuct8sYbbwTqlv2hMEqgHODxeKRO\nnSutYW41gS7W30tEG36vsb77XaCuVWIF4qyRwnaBHnL55U0lNjbW76H2zz//LFdffbUMHDhQUlJS\nzu1PSkqS224bJOHh0WK3/030NNRugV8FvpHQ0EfFZouXtm1vMcbmPwgnT56UFi1uEputj/W/9tb5\nuwU+F4ejvjzxxKSAzfW73W7ZsWOHzJs3T4YOHSqNGjWSyMhIufnmm+Wpp56Sjh27SnDwM15kais6\no15BSuu/cvXV1wdE1j8aRgmUE156aaaEhrYQuEUA0bYAh2ijnN0qfxedNjJC9BL+CIFw0dNG+yQ0\nNFIGDBhQqHZTU1Nl6NChcuWVV8r27dtlx44dUrXqJRIS8pjoHMTeHqp0gXnicFSVDz74oITuiqE0\nycjIkIcffkycznhxOm8V7ZH2s+h0pdtEqWkSEXG5NGjQVJYuXVbi8hw7dkw+/PBDeeyxxyQoyHbR\n2/15pRQm8ILoaaC6AvfJxUbjbLHZ4v9QeQAChVEC5YSTJ0+KUlHW0Da/Tjc/N9FsgVmWMhAJCuok\nDzzwQJHaf+211yQ2NlYiIuJFqYLcAfOWb8Vuj/9DpTKs7KSnp8vixYulVas/S82aV0hsbB2pW7ex\n9OkzUL766qtS9y5LS0uT4OAw0dOleX9/B60Xo1aic2UfF7hB4ImL6kZHX2tGrvlglEA5YfPmzRIe\nXk/yn5M9bI0EduXaFyPadTRY4Dlr30dy9dVtiyxDixY3CUy0FEtLa5QxNFebP1n7c09F/SSwSmJi\nagQ84bfBICJy+vRpCQ2N8PISctJSAm/k2veuwLX5KIHWJZqXuKJSHCVgQkkHkMTERMLDm3NxNA4X\nMBAYAlwBHAemAA2AqoANmAXcDQhJSb8Vqf09e/awY8dP6MXZtYEngWF5atUGlgIngGSgB3AHcDMu\nV2Pee++9IrVtMBREZGQkbncm+a9sjgHq5NmXf1ABj+eEWTUfYIwSCCCpqal4PI48ewWtAMKBp9Ah\nIhoCPwLT0bGE9gBp6BAT93HqVAorV64sdPsvvfRv3O6haKXSG+jJxUHsooD61rYb/RPYC8DZs2N4\n8cXZhW7XYPCFUormzW8AVnipMRR4GTiGDq0yHf2CkpsdBAefpWHDhiUnaCXEKIEAEhUVRVDQmTx7\nh6HfuKcA7dCBvnYDi4Cb0B1/VSAT6AvsRWQxffoMZfbsVwvV/htvvEV29j1+1o4FHOgEI/9n7evJ\nL7/8wv79+wvVrsHgD+PHj8Hp9PaS8STQEj1Sbgy0AB6/oEZ4+BzGjBlOaGhoicpZ2TBKIIA0btyY\nrKxvOT/kHYXOtbMA/VbzADANHVkjAfAAZ4CH0G/sV6GjcPQkPX0DjzzyLEuWvONX2263mzNnjgGX\n+SntSeA0ehqqmbUvhLCwSzl06JCf5zAY/Kd3794EB+8CtuXzbQjwCvp3eRA9EsgdQv0USr3N6NHD\nS17QSoZRAgHksssuo3nz5ui0Cb8Bc9GdfUNgP/AwEI+epmkDOIHL0XH1PkP/6J9G/1v2k57+McOG\njeb48eM+287KyiIoKBitRPzFjo7uMRg9WgGwkZHhKyKlwVB4wsLCmDz5WRyOfpz/vflDFg7H7Qwd\nOoQ6dfLaDgzFxSiBADNhwr1ERr6CDiXtQcdxd6I7+ieALej5/3fRbz/fAR8CV6MDrS4HallnawL0\nYMGC13y2a7PZrK20Qkrsto5JAkDkJDExMYU8h8HgH8OH38199/0Vh+Mm9LPhizM4HN1o3z6Gl1+e\nXNLiVUqMEggw3bp1Iz7+LMHBM6w9C4E+aO+Hp9A2AIBuaAPt5lxH3wv8C52lU5OePoZp0+bg8XgK\nbFcpRZMmrYFPrT1u9LSUG+2dlGltr8b7VFQiLlcSjRo1KvyFGwx+8uKLz/D00yOx2Zpjs41E/x7z\nso/Q0AnYbA25885GrFjxDsHBhRnlGvzFKIEAExISwtq1HxEdPYWgoFeAt4H8jLVHgF/QRjCAZWiv\nns556rUmLc3Ot99+67PtRx8dTWRkjuHtn2jD74vAW9b2s8Ap4E60W96FU1EhIa8yePAgHI68Hk4G\nQ2B5+OEH2bfvZ8aPr0tcXA8iI5sRHd2NqKieREW1ISKiFcOHZ7Ft2wbmz59FSEhp5b+qfCi9zqD8\noJSS8iZTUdi3bx/t23fhwIEktAto9VzfuoAu6E54NjrPTgtgDXqkUB9tTO4EgMPRjbfeGk7v3r0L\nbDMzM5Nq1epx5swazisXf8kAajB8eH+mTp1KZGRkIY83GIqGy+Xi+++/Jzk5GbfbTWxsLC1btjQv\nI4VAKYWIFCl1rxkJlBD169fnp5++JyREuNDLIfe6gZetfZPQxtm65Ed6uoc9e/b4bDM8PJxJk57A\n4bgD7fnjLx7s9iF07vxnsrKyaNSoEQsWLMDtdhfiHAZD0QgJCaFNmzZ0796dXr16cdNNNxkFUIqY\nkUAJU7VqPY4f/wK9Ohj0quDfgE84rxyuQRtmc+Y8j6Gna8YD47Dbb8Lp3ElCQgK1atWiIESEUaPG\n8uabX5OW9hEXjkDyIxObbRiNGx9gw4aV2Gw2vvvuO8aOHUt6ejrTp0+nffv2hb5ug8FQepiRQDmm\nbdu2KPWR9Sln3cAKLhwdrEUnod9mlVpo99J7gRN4PD8wbNgwevTowdmzZwtsTynFv/89g7//vTt2\nexNCQx8nfy+MUyj1EhERTenQIZ0vv/zsnIdRq1at2LBhA+PHj2fw4MHcfvvt/Prrr0W/CQaDofxS\n1KBDJVWowAHk8mP9+vXidF4pOpGMsoLIOa0SKfB2PgG16gusERBRaqr06XOXeDweGTZsmHTt2lWy\ns7P9anvnzp0yevRYcTjiJCqqk0RE/E0cjrslKqqn2Gwx0rPnnfLll18WGFEyLS1NnnnmGYmLi5MJ\nEyaYlJYGQzmEYgSQM9NBJYyIUL9+UxITpwF/KeTRLiIi/sTnn79O27Ztyc7Opnv37jRo0IDZs2ej\nlH+jv9TUVNauXUtycjIul4vY2FjatWtH9eq+porOc/DgQR5//HE+//xznnnmGYYMGWJc9gyGckJx\npoOMEigF3n//v9x11/2kp2/k4miJ3hBstlG0bn2AL774+FyHf+bMGdq1a8fAgQMZN25cicnsje+/\n/56xY8eSmprKjBkzjL3AYCgHFEcJlPn0T97CH2w6KIcXX5wqDkcDgZ1+JHnJlvDwEdKo0bVy5syZ\ni8514MABqVOnjixduvTcvoMHD8pTTz0tdepcJXZ7jISGOiQmppZ06XK7rF27NqBJRDwej7zzzjty\nySWXSN++fWXv3r0BO7fBYCg8mKQyFYO5c+eLzRYjNtsIgYR8k2so9ZJERDSSdu0656sActi6davE\nx8fLBx98IN269Zfw8Bix2UaKTtR9XCBFIFGUmiVO55+kbt2rZPnydwN6PWlpafLPf/5T4uLiZPz4\n8cZeYDCUEUYJVCBy3thjY2tLZGQLiYzsJ07nQImK6iLh4THSo8cdsn79er/e3OfNmydKOSU4eILA\nqQJGFh6BNWK315Xnn58c8GtKSkqSIUOGSM2aNWX+/PnicrkC3obBYPBOcZRAQGwCSqnOwAy0y+kC\nEXkxz/cD0E7voJfHjhaRH72cSwIhU3nH5XKxYcMGDh8+TFZWFjExMVx33XXUqFHDr+OPHDlCs2bX\nc/ToeERG+tlqEg5He6ZPn8CIEf7mHfCf4toL3G43x44d4/Tp0zgcDqpWrYrdbg+4nAbDH40ytQmg\nO/49QD105LME4Mo8ddoA0dZ2Z2BTAecrAT35x6NPn7skOHicwDCBegJRAtcIfGq9/WcJ3C5wqeWa\n+j9r/y6x2WLk0KFDJSKXx+OR//znP1KvXj2/7QVJSUny5JOTJDa2tths8eJ0NhSHo7aEhTnlttsG\nycaNG0s9MbrBUJGgjHMMXwfsFpFEEckG3gF65VE0m0QkJ47BJnSiW0MROXr0KJ9++jFu94PokNVf\nosNEPAP0R69IBp3J7C2gZq6jrwD68eqr80tENqUU/fv35+eff6ZFixa0atWK8ePHc+ZM3oxrOgfC\nkCGjadCgMZMnH+LkyU/IyDjK2bO7SUv7nays/bz/flNuvvkuGjdu7VfoDIPBUDgCoQRqAwdyff6d\ngjv5ezgf79hQBObOXQDchr7N3sJTh6IzmbUl7785I2MMM2e+isvlKjEZ7XY7jz/+ONu3b+fo0aM0\natSI+fPnn4tHlJ6eTvv2XVm69BCZmfvJyJgDNM1zlip4PI+QmrqbXbv+RsuW7di2Lb+sVAaDoaiU\nanxWpVRHdEbpGwuqN3HixHPbHTp0oEOHDiUqV0VjwYJ3rE4zL0fQ+Yt9RRBtTnZ2PF9//TU33XRT\n4AXMRc2aNXnttdfYvHkzY8eO5ZVXXmHatGlMmTKbbdviSU9/E9/Z0ILweO7l9Ol4OnXqRkLCRurW\nzT/YnsFQGVi3bh3r1q0LzMmKOo+UU9Dz/Z/l+jwBGJ9PvaboHuoyH+cL/ITZH4yoqOoCSRetLYCb\nBUbn4x1UJ5dNQJeoqD6ybNmyUpXb4/HI0qVLpVq1ahIcfJnADIGWAuECQ714Nk2ybBo6jEZw8AQZ\nOHB4qcptMJR3KGObwHdAQ6VUPaVUGHAHOkLaOZRSl6DzKQ4Skb0BaLNS43Jl4zs8dcGIhJGVlRV4\n4QpAKUW/fv1o3Lg1bvc4tC/Bk8AwL0fkTbcJbveDLF++jFOnTpW4vAZDZaDYSkBE3MB9wOfADuAd\nEflZKTVSKTXCqvYkOofhbKXUVqWU7zRZBq84nTFA7uTzw9CJu9/D30TzSp0gNjY28ML54MCBA2zc\n+BVwF9Ab6In+aeTHxek2oQZBQbeyaNHikhXUYKgkBCSUtIh8JiKNRORyEXnB2veqiMy1toeLSBUR\nuVZErhGR6wLRbmWlQ4d2BAXlDLa8hacGyEJnDAOdYzjT2j5OZua3tGzZssRlzcvKlSsJCuoKOH3U\n9JZuE9LSBrBkycclIJ3BUPkw+QQqIA8/PAa7fQ6wH513IAGdPCYSiAKWWDUbARHAQXRn6gB+Iyjo\ndbp160F8fHxpi87x48fJyqrpo9ZZ4P+AmV6+r8nx48e9fGcwGAqDyd5cAWnVqhW1a8fxyy8/Ap4C\nau7LZ182oaEv88gjS/L5rrTwtSJ8IgWl2/R9vMFg8BczEqiAKKWYOfNZ7PYRQGHs7EJY2D0EB6cy\na9asMjGuVqlShfDwwz5qrUGPAmpa5QB6Edxk6/tDVKlSpeSENBgqEUYJVFBuvfVWpk6dhMPREfjB\njyOysNmG0qjRL+zf/xMxMTE0a9aML774oqRFvYDOnTvjdn+CnvJxo20WbsCFtlm4KTjdJig1n717\nt/LQQw+xbt06srOzS/UaDIY/FEX1LS2pglknUCiWLHlH7PZYcTjuEvjKihia28/+qAQFvSAREZfK\nLbf0kbNnz5479pNPPpFatWrJww8/LBkZGaUm8y239BX4t8BEaw1AUK4yqcB0m3BQbLYYWb9+vUya\nNElatmwpsbGxMmDAAFmyZImcPHmy1K7DYCgvUNZRRANJZYkiGkhOnDjBa68tYurUOZw9G0JQUH1E\nwlHqBJmZ2+jduw8PPTSaVq1aXXRscnIyI0eOZPfu3bz55ps0bZo3dEPgWb16Nb17P0Bq6ha0B5D/\nBAePZ8CAUyxe/Oq5fQcPHuSjjz5ixYoVrF+/nlatWtGjRw969uxJgwYNAiy9wVD+MOklDQB4PB42\nb97MkSNHyMzMJCYmhmuuuYa4OG9++BoRYdGiRYwbN47x48fz0EMPERSU/0yhiLB27Vrmzn2TffuS\nyMjIICYmmo4dr2PUqHuoWdOX548+R69ed7J6tYf09Lfx3z/hbapUmcC2bRupXTv/8FSpqamsXr2a\nFStW8PHHH1OlShV69uxJjx49aN26dYnkRT527Bjz5i1k2bJPOXnyBEop4uKqMHBgL4YO/RsxMTEB\nb9NgyI1JL2kICL/++qvccMMN0qFDB0lMTLzgO5fLJS+9NFNq124kTmcTgZkCnwmsE3hfbLZREh4e\nI1279pPNmzf7bCs9PV1uvPFWcTi6C5woICGOCLgkKGiGxMTUlB9++MHv63G73bJp0yZ5/PHHpUmT\nJhIfHy9DhgyR9957T1JSUgp9f/Kyd+9e6dt3oNhsMWK3DxVYaWWM2yrwsTgcA8Rmi5EBA4bJ77//\nXuz2DAZvYDKLGQKFy+WS559/XuLj4+XNN98Uj8cjqamp8uc/9xCHo73Al/nYHXLKaVFqpjgc8bJs\n2XKfbWVlZcmwYfdZKTfvEdiS53xHJCjoeXE46kmTJtfLr7/+Wqxr27dvn8ycOVNuvvlmiYyMlC5d\nusjs2bPlwIEDhT7Xpk2bJDq6hgQFPSM6nac3BXZYgoMnSJUqdeXHH38slvwGgzeKowTMdJAhX7Zu\n3cpdd91F48aNOXToNJs3x5OR8RoXr0rO92js9i4sW7aAbt26+ax95MgR5s1byEsv/ZuUlBRCQ6Nx\nu9PxeNLo27cfDz88hhYtWhT7mnJz+vRpVq5cyYcffsgnn3xCvXr1zk0bXXvttSjlfWS9Y8cOrr++\nEykpC4Dufrb4FrGxj7Jly1dceumlgbiEIpGSksKBAwdISUnB6XRSp04doqOjy0weQ2AwNgFDiZCe\nnk7Hjn/h229BZCXwILAaOAlcBjzH+bAOa9AhpA4ArYH7iYi4h337fvZ7ZbLH4+HEiRPn0kvGxcUR\nHh4e6Mu6CJfLxddff82KFStYsWIFaWlpdO/enZ49e9KpUydstvPGaxGhQYMmJCY+gkgq8DrwIzAA\nWGjVSkTndXCiF7YpYDxBQXaaNfsvW7asL/FrysuWLVuYMuUV3n//XUJDaxIU5MTjSSUrK4muXXsw\nbty9tGnTpkDlZyi/GJuAoUTIysqS2NjaAj8IpFrum79Z0xwfCUQKJAokC0QLvCuQKTBOoI3Y7UPl\n2WdfKOvLKDQ7d+6UyZMnS7t27SQqKkp69+4tCxYskMOHD8vatWvF6WxsTYm9L/CBwJg8obD3W+6u\neafNssXhqCMJCQmldi1Hjx6V1q07icNxiQQHPydwJI9MxyUoaKpERDSUJk3aFGlqzFD2YGwChpJg\n+fLlEhnZroD57qYC7wnMFbgh1/5UAbvAcomPv1RcLldZX0qRSU5OlsWLF0u/fv0kOjpaoqNrC8zK\ncx+eyEcJKAHXRfcsOPgZGTx4ZKnInpSUJLVqNZTQ0MfzleXC4paQkBelSpU6smfPnlKRzxA4iqME\nzIphg1dmznyNlJSRXr7NncVsB9As13cOoCEQREZGlcBlQCoDqlSpwqBBg1i6dCl79uwhNfU0MMiP\nIxVwKToH9N3khP52u+/hP/95+1yazZIiNTWV9u27cvToULKzn8Wf7G0u16OcPPk47dt34cSJEyUq\nn6H8YJSAwSv79u0HmuTzjQudxGYIOnH9WSCvcTEKSMHjuZrExMQSlLL0OHnyJDZbNfS1FURVdK6l\nRHS+5xR0/gSAGkBwkeI25by5+cOCBQtJSroUlysKaIVelHd3nlrpwBggHogFOuDxjCY5uS0zZswq\ntHyGiolRAgavZGSkod/qcyNcnMXMCZzJU+80EInLFUFqamqJyllaZGRkEBRk96NmBHAt+vGKB2ah\ncy7p++DxhPLmm2+ycuVKEhISOHjwoNf4Rz/99BMjRtxPXFwdwsLshISEERVVndtuG8TGjRvzVQoi\nwuTJs0lPfxiog/fsbcOBU8Au4AQwHYDMzIeYNWuuiclUSTChpA1ecTqjOHYsb+eek8XsE85PMTQG\nFuWqk4qObtqY0NB3A+KC6PF4WL16NS+//Bp79yaSnp6K0xlFy5ZXM3bsKJo1a+b7JMUkOjoal6uo\nkVcVOuy34Han8PXXX/PRRx9x9OhRjh49SnJyMlFRUVSrVo1q1aoRHh5OQsJeTp48g8czAo/nC3SH\nHkRKylHef38pK1cOpkYNJ3PnTqVTp07nWlq3bh2nToUAN1rtgh6ZJOWSZxfwEfA75xP8XGP9bYrL\nVZ8PP/yQvn37FvF6DRWGohoTSqpgDMPlhr59B4pS03IZD0cKXG8ZfnMbFY8JxFhG4gzLO+h6AZeE\nhdWSzz77rMgyuN1ueemll6VmzcvF6WwqMFtgg7WwbJ0EBz8tdnttadKkraxYsSKAV38xWVlZEhVV\nzfIK+lJgs8BBgccEBlnX7hL4RmCX5R2ULPBXgT9b9+orqVnzcvF4PBdd57Fjx2THjh0yffp0CQ+P\nFZhjeVt5N+bCBxIUFC033niTPProozJ16lTp0qWHXByIL6/xerFl2P+7QFVr+91c38+UQYNGlOj9\nNAQOjHeQoSTYsGGDRERcbnU2iZbHi13AaZVIgbetTmONwJUCDoGOVv0VEh1dR2JjY6Vt27Yyffp0\n+e233/xuPyMjQ3r06C8Ox/WSf4TUnJIt8K44HPVk0qRnA34fPB6PbNy4UW67bZAEB0cINLSUXDUB\n5MJIqJMEloiOfOoUqCXwN8lxzXQ47pKpU6d7bWvbtm0SEREvsNqHN0/u8pOEhsbL4MGDZezYsXLp\npVdZCqQgJfCcJffT1v37nyXvTuv7/8gtt9we8HtpKBmMEjCUCB6PR+rXbyo6Jo6/HdL54nR2ltdf\nf10yMzPlk08+kaFDh0pcXJy0adNGpkyZIvv37/fattvtll697hC7vZdAup9tJonD8SeZPNl7J1tY\nDh06JM2b3yAREZdJUNAU0W/2uds8ITBd4AqB6wQOFCDfYbHZYuTEiRNe27vqqlYCr4t2Q20pEJ6n\n834rlwKOtJSuEnhNoqKqSUZGhgwePFLgFR9KYLp17tyKtYfomFAi8LZ07frXgN1HQ8lSHCVgDMMG\nryileO65x3E47gWOFfLYhTidv9C/f3/CwsLo0qULCxcu5PDhw0ycOJGdO3fSsmVLrrvuOiZPnsy+\nffsuOH7BgoWsWvUr6entgXbk790yH7gc7a3TFVCkpX3KP/7xIgkJCUW86vMkJibSrNn1bN9+K6mp\nv+DxPAzkzWgWC4wFdgK3A9cDe/I5WxYOxwBGjhxJbGxsvu199913/PZbMtrwXpv8DboD0N5GZ6wy\nG716ewgeTzOWLVtG1aqRKPW7j6vLCRkuufblXnCaRM2aVX2cw/CHoKjao6QKZiRQ7hg37v/EpwI4\nBQAADpZJREFU4WgmkOTXG7lSr0tUVHXZuXNngefNysqSVatWyYgRIyQ+Pl5atGghL7zwguzevdsa\ngawS76tyvxA9HfOzNZ0xWqC96AVZz8rAgcOLdc2nT5+WevX+JMHB0/y65vNljsDlcmFQuRRxOLrJ\nrbf2KXDhXP/+QyQo6EUfb/B5S0fRUzoi8L44HDXEbrdLUFB10fYJlzWSymu3yLbk/Kf1eYNAlOTY\nMpzOJrJq1api3UND6YGZDjKUJB6PRyZOfFbs9poSFPS8wNF8OiOPwHpxOPpLtWqXyk8//VSoNrKz\ns2XNmjUyatQoiYmJkaCguqJtEd46w0cE7s31+aA1LfKr5Ey7FCfL2L/+NUXs9v7ifVpGBP4jcJXV\neTYW+K+1f7DAMwInRKlpEhHRQAYMuFuysrIKvMdhYY587m1BSmC/QIj1VwSyJSwsTnbt2iVXXXWd\nwIdScPa2HaJtG05L/g+s/V9KrVpXiNvtLvL9M5QuZa4E0FHEdgK/AOO91JmJXmKaADQv4FwldJsM\nxeX777+XO+4YKjZbtDgc/QWeFHhWQkIeEafzaqlV6wqZNm2GnDp1qljt3HXXPaLUv3x0hnmVwO9W\nZ7dCQCQiop/MmzevSO273W6pUeMygU3ifSSSJBAm5+0lH4uenz8mOp9AjISHR0vv3gPkyy+/vMgb\nKC9nz56VkBB7Ph19QUrgadEjgfP7oqKay+bNm+X111+XiIgbRb/x53est+IWu72bTJkyrUj3zlA2\nFEcJFHudgFIqCL0a5s/AQeA7pdQHIrIzV50uwGUicrlSqjXwb6BNcds2lC4tWrRgyZKFnDgxhXff\nfZekpIOkpaVQpUpVWracQadOnQIShfLXX39HpI+PWp3R8+Oj0HPiT6MXZ6UBkJp6FQcO+JoXz59V\nq1aRmhoNXId3P/vf0faAW6zPXdGLxPYCrbHZLmfOnDEMGTLkovPnREs9fPjwubJv3z48nsLeuzeA\nJy7Yo1Qo2dnZDBgwgPnz3+a77+4lM3MO/q0LFUJDH+Pyy08wZsyoQspiqKgEYrHYdcBuEUkEUEq9\nA/RCjwxy6AUsBhCRb5RS0Uqp6iJyJADtG0qZuLg4hg8fXmLnT0tLB3ytzP0zMBHoizaUjgUi0Quq\nAOy8/vo8tm//EafTSWRkJJGRkee289uXs/3BB5+QknI7FxpK89ISuAq94KorsAJtvNYG14yMAbz6\n6iJ27959QWd/+PBhjh49SlRUFDVq1DhXqlevjkgmkIV/ORu+Ag4Bt12w1+VKJjY2ltDQUD7+eBnt\n23dl1667SE+fjVZa3jiDzTaOunW/Zc2aVdjt/qyMNvwRCIQSqI0OIp/D72jFUFCdJGufUQKGi4iJ\niUaHM/DFaKuAnmn8J3A1AEFBp+jYsR3du3fn7NmzpKSkkJKSwtmzZzl8+PC57dz7c7ZPnswiJ4SC\nd4LQgeTuBDLQYTSWcV55xXPoUDJ2u53rr7/+XEdfo0aNcyuC8/K//21hy5YPgH6AG8i2/rqATPTj\nmrNKexFaAUTkOkMCYWFZNGjQAICoqCi+/noVI0eOZdmyBijVl/T0MeiQFgrtGbSd8PA5KPUOXbp0\nZdGi9URGRvq68YY/EOUybMTEiRPPbXfo0IEOHTqUmSyG0ufGG69h06Y1ZGb2wXtn6EK7YjYGfgNG\noEcDOkRFRMRa+vefSNeuXQvd/oAB97Bkia8on6uBR4H16HAL3wM9gc/QowE3zZo144knnvB+ijyM\nHz+Ge+6ZTUpKP7RCm8T50chbwD+Ap9D3YDnw3gXH22xzuP/+kYSEnH+s7XY7ixe/ypQpz1jZ227n\n+PHfCQ2NJDv7LNHR1Rgz5h7GjNlOrVq1/JbVULasW7cucNF5i2pMyCnouf3Pcn2eQB7jMNoG8Ndc\nn3cC1b2cL/BWE0OF4sCBA1bYhDMFeLecEh3qwClQU+D/5PzCp2+lWrX6Rc5j8MgjE0SpJ30YaKcI\n9M1Tp7fAVNFuss/J6NEPFqrdzMxMiY6uITpZfWGMuSJwTMLDY+TgwYM+28nIyJBjx45JRkZGke6P\nofxBMQzDgVgs9h3QUClVTykVBtyBniDNzQpgMIBSqg1wSow9wOCFOnXq0L59R7Th8x/owGvuXOUp\n9Bv/NrQ94CD6zVm/NdvtrzB27CiCg33F0M+f/v374nC8mavdDC4cibjR4Zk3WDIAbLU+NwWEiIjF\n3HFH4YKvhYWFMWXKczgct1O4xXkZOBx9GTVqBDVr1vRZOzw8nKpVq5ZK6k5DBaCo2iN3Qbtq7EJP\nzE6w9o0ERuSqMws9ft8GXFvAuUpKWRoqEN98843Y7fEC2wv5Rrxc4uJqS3JycrHab9SopeX2WZCf\n/Sui4whFCVwmOhSDCKyRevUa+3QL9caECU+Jw3Gl6DUPvq73hDgcHaRXrzuNX38lhrJeJxDIYpSA\nIYdFi94Qh6O2aL97fxTAUnE642XLli3FbnvhwoUSEXFTEfzsXeJw/EVefnlWsdqfPn2m2GwxYrPd\nLTpaad52dktY2ENis1WRe+99qEKn8DQUn+IoAaWPLz8opaS8yWQoO5YtW86QIaNwuweQmTka7ZaZ\nGwHW43DMxm7/mtWrP6R58+bFbjc7O5uOHbvx/ff1C+VnHxY2lqZNE9iw4fNiT7ccPXqUefMWMmPG\nv8nMjCAoqDYQjMhRPJ4DDB9+N/ffP5L69esXqx1DxUcphYgUaZGOUQKGcs+BAweYPXsec+bMw+Np\nRFZWE1yuCMLCThMcvJ7YWGHcuDEMHjwoIAlscjhz5gzt23dl5846ZGS8wsXB43JzCpvtQRo0+JkN\nG1Z6DRJXFNxuN1u3biU5ORmPx0NsbCzXXHMNNpstYG0YKjZGCRgqBVlZWXz66ackJiaSmppKVFQU\nTZo0oV27dgFZqZwfGRkZjBz5d5YufQelepOePhq9UCwIbThOwG6fg8hyevfuy/z5M4mIiCj4pAZD\ngDFKwGAoYZKTk1mw4DVmzHiVI0f2ExrqIDs7japV6/LAAyMYMWIY1apVK2sxDZUUowQMhlLE5XJx\n9uxZnE7nBQuzDIaywigBg8FgqMQURwmYzGIGg8FQiTFKwGAwGCoxRgkYDAZDJcYoAYPBYKjEGCVg\nMBgMlRijBAwGg6ESY5SAwWAwVGKMEjAYDIZKjFECBoPBUIkxSsBgMBgqMUYJGAwGQyXGKAGDwWCo\nxBglYDAYDJUYowQMBoOhEmOUgMFgMFRijBIwGAyGSkyxlIBSKlYp9blSapdSaqVS6qIs30qpOkqp\ntUqpHUqpH5VSDxSnTYPBYDAEjuKOBCYAq0WkEbAWeCyfOi7gIRFpDFwP3KuUurKY7ZZL1q1bV9Yi\nFAsjf9li5C9bKrr8RaW4SqAXsMjaXgT0zltBRA6LSIK1fRb4GahdzHbLJRX9R2TkL1uM/GVLRZe/\nqBRXCVQTkSOgO3ugWkGVlVKXAs2Bb4rZrsFgMBgCQIivCkqpVUD13LsAAZ7Ip7rXDPFKKSewHHjQ\nGhEYDAaDoYxRIl77bd8HK/Uz0EFEjiilagBfiMhV+dQLAT4CPhWRl3ycs+gCGQwGQyVFRFRRjvM5\nEvDBCmAI8CLwN+ADL/UWAj/5UgBQ9AsxGAwGQ+Ep7kggDlgK1AUSgf4ickopVROYJyLdlVI3AOuB\nH9HTRQI8LiKfFVt6g8FgMBSLYikBg8FgMFRsynTFcEVdbKaU6qyU2qmU+kUpNd5LnZlKqd1KqQSl\nVPPSlrEgfMmvlBqglNpmlQ1KqSZlIac3/Ln/Vr1WSqlspVTf0pTPF37+fjoopbYqpbYrpb4obRm9\n4cdvJ0optcL63f+olBpSBmJ6RSm1QCl1RCn1QwF1yvOzW6D8RXp2RaTMCtqW8Ki1PR54IZ86NYDm\n1rYT2AVcWYYyBwF7gHpAKJCQVx6gC/Cxtd0a2FSW97kI8rcBoq3tzhVN/lz11qAdEvqWtdyFvP/R\nwA6gtvW5alnLXQjZHwOez5EbOA6ElLXsueS7Ee2m/oOX78vts+un/IV+dss6dlBFXGx2HbBbRBJF\nJBt4B30duekFLAYQkW+AaKVUdcoHPuUXkU0ictr6uInytbjPn/sPcD/aJfloaQrnB/7IPwB4V0SS\nAEQkuZRl9IY/sgsQaW1HAsdFxFWKMhaIiGwAThZQpTw/uz7lL8qzW9ZKoCIuNqsNHMj1+XcuvtF5\n6yTlU6es8Ef+3NwDfFqiEhUOn/IrpWoBvUVkDnpdS3nCn/t/BRCnlPpCKfWdUmpQqUlXMP7IPgv4\nk1LqILANeLCUZAsU5fnZLSx+PbvFdRH1iVlsVnFRSnUEhqKHoBWJGejpxRzKmyLwRQhwLdAJiAA2\nKqU2isieshXLL24FtopIJ6XUZcAqpVRT88yWLoV5dktcCYjIX7x9Zxk4qsv5xWb5Dt2txWbLgTdE\nxNtahNIiCbgk1+c61r68der6qFNW+CM/SqmmwFygs4gUNHwubfyRvyXwjlJKoeeluyilskVkRSnJ\nWBD+yP87kCwiGUCGUmo90Aw9H1+W+CP7UOB5ABHZq5TaB1wJfF8qEhaf8vzs+kVhn92yng7KWWwG\nAVpsVgp8BzRUStVTSoUBd6CvIzcrgMEASqk2wKmcaa9ygE/5lVKXAO8Cg0RkbxnIWBA+5ReRBlap\nj355GFNOFAD49/v5ALhRKRWslHKgDZQ/l7Kc+eGP7InAzQDWXPoVwK+lKqVvFN5Hh+X52c3Bq/xF\nenbL2NIdB6xGe/x8DsRY+2sCH1nbNwButCfCVmALWsOVpdydLZl3AxOsfSOBEbnqzEK/uW0Dri1L\neQsrPzAP7dWxxbrn35a1zIW9/7nqLqQceQcV4vfzCNpD6Afg/rKWuRC/nZrASkvuH4A7y1rmPPK/\nDRwEMoHf0COXivTsFih/UZ5ds1jMYDAYKjFlPR1kMBgMhjLEKAGDwWCoxBglYDAYDJUYowQMBoOh\nEmOUgMFgMFRijBIwGAyGSoxRAgaDwVCJMUrAYDAYKjH/D2+PHcBgsmN2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e76f050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%run d_explore.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
arcyfelix/Courses | 18-03-07-Deep Learning With Python by François Chollet/Chapter 8.3 - Neural Style Transfer.ipynb | 2 | 1924863 | null | apache-2.0 |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-lm/atmos.ipynb | 1 | 209009 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Atmos \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: MOHC \n",
"**Source ID**: HADGEM3-GC31-LM \n",
"**Topic**: Atmos \n",
"**Sub-Topics**: Dynamical Core, Radiation, Turbulence Convection, Microphysics Precipitation, Cloud Scheme, Observation Simulation, Gravity Waves, Solar, Volcanos. \n",
"**Properties**: 156 (127 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmos?client=jupyter-notebook) \n",
"**Initialized From**: -- \n",
"\n",
"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n",
"**Notebook Initialised**: 2018-02-15 16:54:14"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Document Setup \n",
"**IMPORTANT: to be executed each time you run the notebook** "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# DO NOT EDIT ! \n",
"from pyesdoc.ipython.model_topic import NotebookOutput \n",
"\n",
"# DO NOT EDIT ! \n",
"DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-lm', 'atmos')"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Authors \n",
"*Set document authors*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_author(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Contributors \n",
"*Specify document contributors* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_contributor(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Publication \n",
"*Specify document publication status* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set publication status: \n",
"# 0=do not publish, 1=publish. \n",
"DOC.set_publication_status(0)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Table of Contents \n",
"[1. Key Properties --> Overview](#1.-Key-Properties--->-Overview) \n",
"[2. Key Properties --> Resolution](#2.-Key-Properties--->-Resolution) \n",
"[3. Key Properties --> Timestepping](#3.-Key-Properties--->-Timestepping) \n",
"[4. Key Properties --> Orography](#4.-Key-Properties--->-Orography) \n",
"[5. Grid --> Discretisation](#5.-Grid--->-Discretisation) \n",
"[6. Grid --> Discretisation --> Horizontal](#6.-Grid--->-Discretisation--->-Horizontal) \n",
"[7. Grid --> Discretisation --> Vertical](#7.-Grid--->-Discretisation--->-Vertical) \n",
"[8. Dynamical Core](#8.-Dynamical-Core) \n",
"[9. Dynamical Core --> Top Boundary](#9.-Dynamical-Core--->-Top-Boundary) \n",
"[10. Dynamical Core --> Lateral Boundary](#10.-Dynamical-Core--->-Lateral-Boundary) \n",
"[11. Dynamical Core --> Diffusion Horizontal](#11.-Dynamical-Core--->-Diffusion-Horizontal) \n",
"[12. Dynamical Core --> Advection Tracers](#12.-Dynamical-Core--->-Advection-Tracers) \n",
"[13. Dynamical Core --> Advection Momentum](#13.-Dynamical-Core--->-Advection-Momentum) \n",
"[14. Radiation](#14.-Radiation) \n",
"[15. Radiation --> Shortwave Radiation](#15.-Radiation--->-Shortwave-Radiation) \n",
"[16. Radiation --> Shortwave GHG](#16.-Radiation--->-Shortwave-GHG) \n",
"[17. Radiation --> Shortwave Cloud Ice](#17.-Radiation--->-Shortwave-Cloud-Ice) \n",
"[18. Radiation --> Shortwave Cloud Liquid](#18.-Radiation--->-Shortwave-Cloud-Liquid) \n",
"[19. Radiation --> Shortwave Cloud Inhomogeneity](#19.-Radiation--->-Shortwave-Cloud-Inhomogeneity) \n",
"[20. Radiation --> Shortwave Aerosols](#20.-Radiation--->-Shortwave-Aerosols) \n",
"[21. Radiation --> Shortwave Gases](#21.-Radiation--->-Shortwave-Gases) \n",
"[22. Radiation --> Longwave Radiation](#22.-Radiation--->-Longwave-Radiation) \n",
"[23. Radiation --> Longwave GHG](#23.-Radiation--->-Longwave-GHG) \n",
"[24. Radiation --> Longwave Cloud Ice](#24.-Radiation--->-Longwave-Cloud-Ice) \n",
"[25. Radiation --> Longwave Cloud Liquid](#25.-Radiation--->-Longwave-Cloud-Liquid) \n",
"[26. Radiation --> Longwave Cloud Inhomogeneity](#26.-Radiation--->-Longwave-Cloud-Inhomogeneity) \n",
"[27. Radiation --> Longwave Aerosols](#27.-Radiation--->-Longwave-Aerosols) \n",
"[28. Radiation --> Longwave Gases](#28.-Radiation--->-Longwave-Gases) \n",
"[29. Turbulence Convection](#29.-Turbulence-Convection) \n",
"[30. Turbulence Convection --> Boundary Layer Turbulence](#30.-Turbulence-Convection--->-Boundary-Layer-Turbulence) \n",
"[31. Turbulence Convection --> Deep Convection](#31.-Turbulence-Convection--->-Deep-Convection) \n",
"[32. Turbulence Convection --> Shallow Convection](#32.-Turbulence-Convection--->-Shallow-Convection) \n",
"[33. Microphysics Precipitation](#33.-Microphysics-Precipitation) \n",
"[34. Microphysics Precipitation --> Large Scale Precipitation](#34.-Microphysics-Precipitation--->-Large-Scale-Precipitation) \n",
"[35. Microphysics Precipitation --> Large Scale Cloud Microphysics](#35.-Microphysics-Precipitation--->-Large-Scale-Cloud-Microphysics) \n",
"[36. Cloud Scheme](#36.-Cloud-Scheme) \n",
"[37. Cloud Scheme --> Optical Cloud Properties](#37.-Cloud-Scheme--->-Optical-Cloud-Properties) \n",
"[38. Cloud Scheme --> Sub Grid Scale Water Distribution](#38.-Cloud-Scheme--->-Sub-Grid-Scale-Water-Distribution) \n",
"[39. Cloud Scheme --> Sub Grid Scale Ice Distribution](#39.-Cloud-Scheme--->-Sub-Grid-Scale-Ice-Distribution) \n",
"[40. Observation Simulation](#40.-Observation-Simulation) \n",
"[41. Observation Simulation --> Isscp Attributes](#41.-Observation-Simulation--->-Isscp-Attributes) \n",
"[42. Observation Simulation --> Cosp Attributes](#42.-Observation-Simulation--->-Cosp-Attributes) \n",
"[43. Observation Simulation --> Radar Inputs](#43.-Observation-Simulation--->-Radar-Inputs) \n",
"[44. Observation Simulation --> Lidar Inputs](#44.-Observation-Simulation--->-Lidar-Inputs) \n",
"[45. Gravity Waves](#45.-Gravity-Waves) \n",
"[46. Gravity Waves --> Orographic Gravity Waves](#46.-Gravity-Waves--->-Orographic-Gravity-Waves) \n",
"[47. Gravity Waves --> Non Orographic Gravity Waves](#47.-Gravity-Waves--->-Non-Orographic-Gravity-Waves) \n",
"[48. Solar](#48.-Solar) \n",
"[49. Solar --> Solar Pathways](#49.-Solar--->-Solar-Pathways) \n",
"[50. Solar --> Solar Constant](#50.-Solar--->-Solar-Constant) \n",
"[51. Solar --> Orbital Parameters](#51.-Solar--->-Orbital-Parameters) \n",
"[52. Solar --> Insolation Ozone](#52.-Solar--->-Insolation-Ozone) \n",
"[53. Volcanos](#53.-Volcanos) \n",
"[54. Volcanos --> Volcanoes Treatment](#54.-Volcanos--->-Volcanoes-Treatment) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties --> Overview \n",
"*Top level key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.model_overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.2. Model Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of atmosphere model code (CAM 4.0, ARPEGE 3.2,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.model_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.3. Model Family\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of atmospheric model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.model_family') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"AGCM\" \n",
"# \"ARCM\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Basic Approximations\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Basic approximations made in the atmosphere.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"primitive equations\" \n",
"# \"non-hydrostatic\" \n",
"# \"anelastic\" \n",
"# \"Boussinesq\" \n",
"# \"hydrostatic\" \n",
"# \"quasi-hydrostatic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Resolution \n",
"*Characteristics of the model resolution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Horizontal Resolution Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *This is a string usually used by the modelling group to describe the resolution of the model grid, e.g. T42, N48.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Canonical Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Expression quoted for gross comparisons of resolution, e.g. 2.5 x 3.75 degrees lat-lon.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.3. Range Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Range of horizontal resolution with spatial details, eg. 1 deg (Equator) - 0.5 deg*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.4. Number Of Vertical Levels\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of vertical levels resolved on the computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.5. High Top\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the atmosphere have a high-top? High-Top atmospheres have a fully resolved stratosphere with a model top above the stratopause.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.high_top') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Timestepping \n",
"*Characteristics of the atmosphere model time stepping*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Timestep Dynamics\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Timestep for the dynamics, e.g. 30 min.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Timestep Shortwave Radiative Transfer\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Timestep for the shortwave radiative transfer, e.g. 1.5 hours.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.3. Timestep Longwave Radiative Transfer\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Timestep for the longwave radiative transfer, e.g. 3 hours.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Orography \n",
"*Characteristics of the model orography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time adaptation of the orography.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.orography.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"present day\" \n",
"# \"modified\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Changes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *If the orography type is modified describe the time adaptation changes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.orography.changes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"related to ice sheets\" \n",
"# \"related to tectonics\" \n",
"# \"modified mean\" \n",
"# \"modified variance if taken into account in model (cf gravity waves)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 5. Grid --> Discretisation \n",
"*Atmosphere grid discretisation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of grid discretisation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 6. Grid --> Discretisation --> Horizontal \n",
"*Atmosphere discretisation in the horizontal*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"spectral\" \n",
"# \"fixed grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"finite elements\" \n",
"# \"finite volumes\" \n",
"# \"finite difference\" \n",
"# \"centered finite difference\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. Scheme Order\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation function order*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"second\" \n",
"# \"third\" \n",
"# \"fourth\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. Horizontal Pole\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Horizontal discretisation pole singularity treatment*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"filter\" \n",
"# \"pole rotation\" \n",
"# \"artificial island\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.5. Grid Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal grid type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Gaussian\" \n",
"# \"Latitude-Longitude\" \n",
"# \"Cubed-Sphere\" \n",
"# \"Icosahedral\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Grid --> Discretisation --> Vertical \n",
"*Atmosphere discretisation in the vertical*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Coordinate Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Type of vertical coordinate system*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"isobaric\" \n",
"# \"sigma\" \n",
"# \"hybrid sigma-pressure\" \n",
"# \"hybrid pressure\" \n",
"# \"vertically lagrangian\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Dynamical Core \n",
"*Characteristics of the dynamical core*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of atmosphere dynamical core*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the dynamical core of the model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Timestepping Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Timestepping framework type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Adams-Bashforth\" \n",
"# \"explicit\" \n",
"# \"implicit\" \n",
"# \"semi-implicit\" \n",
"# \"leap frog\" \n",
"# \"multi-step\" \n",
"# \"Runge Kutta fifth order\" \n",
"# \"Runge Kutta second order\" \n",
"# \"Runge Kutta third order\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of the model prognostic variables*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"surface pressure\" \n",
"# \"wind components\" \n",
"# \"divergence/curl\" \n",
"# \"temperature\" \n",
"# \"potential temperature\" \n",
"# \"total water\" \n",
"# \"water vapour\" \n",
"# \"water liquid\" \n",
"# \"water ice\" \n",
"# \"total water moments\" \n",
"# \"clouds\" \n",
"# \"radiation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Dynamical Core --> Top Boundary \n",
"*Type of boundary layer at the top of the model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Top Boundary Condition\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Top boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"sponge layer\" \n",
"# \"radiation boundary condition\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.2. Top Heat\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Top boundary heat treatment*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.3. Top Wind\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Top boundary wind treatment*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 10. Dynamical Core --> Lateral Boundary \n",
"*Type of lateral boundary condition (if the model is a regional model)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Condition\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Type of lateral boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"sponge layer\" \n",
"# \"radiation boundary condition\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Dynamical Core --> Diffusion Horizontal \n",
"*Horizontal diffusion scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Horizontal diffusion scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal diffusion scheme method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"iterated Laplacian\" \n",
"# \"bi-harmonic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Dynamical Core --> Advection Tracers \n",
"*Tracer advection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Tracer advection scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Heun\" \n",
"# \"Roe and VanLeer\" \n",
"# \"Roe and Superbee\" \n",
"# \"Prather\" \n",
"# \"UTOPIA\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Scheme Characteristics\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Tracer advection scheme characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Eulerian\" \n",
"# \"modified Euler\" \n",
"# \"Lagrangian\" \n",
"# \"semi-Lagrangian\" \n",
"# \"cubic semi-Lagrangian\" \n",
"# \"quintic semi-Lagrangian\" \n",
"# \"mass-conserving\" \n",
"# \"finite volume\" \n",
"# \"flux-corrected\" \n",
"# \"linear\" \n",
"# \"quadratic\" \n",
"# \"quartic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.3. Conserved Quantities\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Tracer advection scheme conserved quantities*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"dry mass\" \n",
"# \"tracer mass\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.4. Conservation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Tracer advection scheme conservation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"conservation fixer\" \n",
"# \"Priestley algorithm\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Dynamical Core --> Advection Momentum \n",
"*Momentum advection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Momentum advection schemes name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"VanLeer\" \n",
"# \"Janjic\" \n",
"# \"SUPG (Streamline Upwind Petrov-Galerkin)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Scheme Characteristics\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Momentum advection scheme characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"2nd order\" \n",
"# \"4th order\" \n",
"# \"cell-centred\" \n",
"# \"staggered grid\" \n",
"# \"semi-staggered grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.3. Scheme Staggering Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Momentum advection scheme staggering type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Arakawa B-grid\" \n",
"# \"Arakawa C-grid\" \n",
"# \"Arakawa D-grid\" \n",
"# \"Arakawa E-grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.4. Conserved Quantities\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Momentum advection scheme conserved quantities*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Angular momentum\" \n",
"# \"Horizontal momentum\" \n",
"# \"Enstrophy\" \n",
"# \"Mass\" \n",
"# \"Total energy\" \n",
"# \"Vorticity\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.5. Conservation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Momentum advection scheme conservation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"conservation fixer\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Radiation \n",
"*Characteristics of the atmosphere radiation process*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Aerosols\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Aerosols whose radiative effect is taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.aerosols') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"sulphate\" \n",
"# \"nitrate\" \n",
"# \"sea salt\" \n",
"# \"dust\" \n",
"# \"ice\" \n",
"# \"organic\" \n",
"# \"BC (black carbon / soot)\" \n",
"# \"SOA (secondary organic aerosols)\" \n",
"# \"POM (particulate organic matter)\" \n",
"# \"polar stratospheric ice\" \n",
"# \"NAT (nitric acid trihydrate)\" \n",
"# \"NAD (nitric acid dihydrate)\" \n",
"# \"STS (supercooled ternary solution aerosol particle)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 15. Radiation --> Shortwave Radiation \n",
"*Properties of the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of shortwave radiation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.3. Spectral Integration\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Shortwave radiation scheme spectral integration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"wide-band model\" \n",
"# \"correlated-k\" \n",
"# \"exponential sum fitting\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.4. Transport Calculation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Shortwave radiation transport calculation methods*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"two-stream\" \n",
"# \"layer interaction\" \n",
"# \"bulk\" \n",
"# \"adaptive\" \n",
"# \"multi-stream\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.5. Spectral Intervals\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Shortwave radiation scheme number of spectral intervals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 16. Radiation --> Shortwave GHG \n",
"*Representation of greenhouse gases in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Greenhouse Gas Complexity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Complexity of greenhouse gases whose shortwave radiative effects are taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CO2\" \n",
"# \"CH4\" \n",
"# \"N2O\" \n",
"# \"CFC-11 eq\" \n",
"# \"CFC-12 eq\" \n",
"# \"HFC-134a eq\" \n",
"# \"Explicit ODSs\" \n",
"# \"Explicit other fluorinated gases\" \n",
"# \"O3\" \n",
"# \"H2O\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.2. ODS\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Ozone depleting substances whose shortwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CFC-12\" \n",
"# \"CFC-11\" \n",
"# \"CFC-113\" \n",
"# \"CFC-114\" \n",
"# \"CFC-115\" \n",
"# \"HCFC-22\" \n",
"# \"HCFC-141b\" \n",
"# \"HCFC-142b\" \n",
"# \"Halon-1211\" \n",
"# \"Halon-1301\" \n",
"# \"Halon-2402\" \n",
"# \"methyl chloroform\" \n",
"# \"carbon tetrachloride\" \n",
"# \"methyl chloride\" \n",
"# \"methylene chloride\" \n",
"# \"chloroform\" \n",
"# \"methyl bromide\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.3. Other Flourinated Gases\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Other flourinated gases whose shortwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"HFC-134a\" \n",
"# \"HFC-23\" \n",
"# \"HFC-32\" \n",
"# \"HFC-125\" \n",
"# \"HFC-143a\" \n",
"# \"HFC-152a\" \n",
"# \"HFC-227ea\" \n",
"# \"HFC-236fa\" \n",
"# \"HFC-245fa\" \n",
"# \"HFC-365mfc\" \n",
"# \"HFC-43-10mee\" \n",
"# \"CF4\" \n",
"# \"C2F6\" \n",
"# \"C3F8\" \n",
"# \"C4F10\" \n",
"# \"C5F12\" \n",
"# \"C6F14\" \n",
"# \"C7F16\" \n",
"# \"C8F18\" \n",
"# \"c-C4F8\" \n",
"# \"NF3\" \n",
"# \"SF6\" \n",
"# \"SO2F2\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 17. Radiation --> Shortwave Cloud Ice \n",
"*Shortwave radiative properties of ice crystals in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 17.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with cloud ice crystals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud ice crystals in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"bi-modal size distribution\" \n",
"# \"ensemble of ice crystals\" \n",
"# \"mean projected area\" \n",
"# \"ice water path\" \n",
"# \"crystal asymmetry\" \n",
"# \"crystal aspect ratio\" \n",
"# \"effective crystal radius\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud ice crystals in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 18. Radiation --> Shortwave Cloud Liquid \n",
"*Shortwave radiative properties of liquid droplets in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 18.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with cloud liquid droplets*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud liquid droplets in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"cloud droplet number concentration\" \n",
"# \"effective cloud droplet radii\" \n",
"# \"droplet size distribution\" \n",
"# \"liquid water path\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud liquid droplets in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"geometric optics\" \n",
"# \"Mie theory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 19. Radiation --> Shortwave Cloud Inhomogeneity \n",
"*Cloud inhomogeneity in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 19.1. Cloud Inhomogeneity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method for taking into account horizontal cloud inhomogeneity*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Monte Carlo Independent Column Approximation\" \n",
"# \"Triplecloud\" \n",
"# \"analytic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 20. Radiation --> Shortwave Aerosols \n",
"*Shortwave radiative properties of aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 20.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of aerosols in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"number concentration\" \n",
"# \"effective radii\" \n",
"# \"size distribution\" \n",
"# \"asymmetry\" \n",
"# \"aspect ratio\" \n",
"# \"mixing state\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to aerosols in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 21. Radiation --> Shortwave Gases \n",
"*Shortwave radiative properties of gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 21.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 22. Radiation --> Longwave Radiation \n",
"*Properties of the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 22.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of longwave radiation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the longwave radiation scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.3. Spectral Integration\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Longwave radiation scheme spectral integration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"wide-band model\" \n",
"# \"correlated-k\" \n",
"# \"exponential sum fitting\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.4. Transport Calculation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Longwave radiation transport calculation methods*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"two-stream\" \n",
"# \"layer interaction\" \n",
"# \"bulk\" \n",
"# \"adaptive\" \n",
"# \"multi-stream\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.5. Spectral Intervals\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Longwave radiation scheme number of spectral intervals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 23. Radiation --> Longwave GHG \n",
"*Representation of greenhouse gases in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 23.1. Greenhouse Gas Complexity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Complexity of greenhouse gases whose longwave radiative effects are taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CO2\" \n",
"# \"CH4\" \n",
"# \"N2O\" \n",
"# \"CFC-11 eq\" \n",
"# \"CFC-12 eq\" \n",
"# \"HFC-134a eq\" \n",
"# \"Explicit ODSs\" \n",
"# \"Explicit other fluorinated gases\" \n",
"# \"O3\" \n",
"# \"H2O\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.2. ODS\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Ozone depleting substances whose longwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CFC-12\" \n",
"# \"CFC-11\" \n",
"# \"CFC-113\" \n",
"# \"CFC-114\" \n",
"# \"CFC-115\" \n",
"# \"HCFC-22\" \n",
"# \"HCFC-141b\" \n",
"# \"HCFC-142b\" \n",
"# \"Halon-1211\" \n",
"# \"Halon-1301\" \n",
"# \"Halon-2402\" \n",
"# \"methyl chloroform\" \n",
"# \"carbon tetrachloride\" \n",
"# \"methyl chloride\" \n",
"# \"methylene chloride\" \n",
"# \"chloroform\" \n",
"# \"methyl bromide\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.3. Other Flourinated Gases\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Other flourinated gases whose longwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"HFC-134a\" \n",
"# \"HFC-23\" \n",
"# \"HFC-32\" \n",
"# \"HFC-125\" \n",
"# \"HFC-143a\" \n",
"# \"HFC-152a\" \n",
"# \"HFC-227ea\" \n",
"# \"HFC-236fa\" \n",
"# \"HFC-245fa\" \n",
"# \"HFC-365mfc\" \n",
"# \"HFC-43-10mee\" \n",
"# \"CF4\" \n",
"# \"C2F6\" \n",
"# \"C3F8\" \n",
"# \"C4F10\" \n",
"# \"C5F12\" \n",
"# \"C6F14\" \n",
"# \"C7F16\" \n",
"# \"C8F18\" \n",
"# \"c-C4F8\" \n",
"# \"NF3\" \n",
"# \"SF6\" \n",
"# \"SO2F2\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 24. Radiation --> Longwave Cloud Ice \n",
"*Longwave radiative properties of ice crystals in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 24.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with cloud ice crystals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 24.2. Physical Reprenstation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud ice crystals in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"bi-modal size distribution\" \n",
"# \"ensemble of ice crystals\" \n",
"# \"mean projected area\" \n",
"# \"ice water path\" \n",
"# \"crystal asymmetry\" \n",
"# \"crystal aspect ratio\" \n",
"# \"effective crystal radius\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 24.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud ice crystals in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 25. Radiation --> Longwave Cloud Liquid \n",
"*Longwave radiative properties of liquid droplets in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 25.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with cloud liquid droplets*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud liquid droplets in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"cloud droplet number concentration\" \n",
"# \"effective cloud droplet radii\" \n",
"# \"droplet size distribution\" \n",
"# \"liquid water path\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud liquid droplets in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"geometric optics\" \n",
"# \"Mie theory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 26. Radiation --> Longwave Cloud Inhomogeneity \n",
"*Cloud inhomogeneity in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 26.1. Cloud Inhomogeneity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method for taking into account horizontal cloud inhomogeneity*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_inhomogeneity.cloud_inhomogeneity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Monte Carlo Independent Column Approximation\" \n",
"# \"Triplecloud\" \n",
"# \"analytic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 27. Radiation --> Longwave Aerosols \n",
"*Longwave radiative properties of aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 27.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of aerosols in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"number concentration\" \n",
"# \"effective radii\" \n",
"# \"size distribution\" \n",
"# \"asymmetry\" \n",
"# \"aspect ratio\" \n",
"# \"mixing state\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to aerosols in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 28. Radiation --> Longwave Gases \n",
"*Longwave radiative properties of gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 28.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_gases.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 29. Turbulence Convection \n",
"*Atmosphere Convective Turbulence and Clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 29.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of atmosphere convection and turbulence*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 30. Turbulence Convection --> Boundary Layer Turbulence \n",
"*Properties of the boundary layer turbulence scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 30.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Boundary layer turbulence scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Mellor-Yamada\" \n",
"# \"Holtslag-Boville\" \n",
"# \"EDMF\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.2. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Boundary layer turbulence scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"TKE prognostic\" \n",
"# \"TKE diagnostic\" \n",
"# \"TKE coupled with water\" \n",
"# \"vertical profile of Kz\" \n",
"# \"non-local diffusion\" \n",
"# \"Monin-Obukhov similarity\" \n",
"# \"Coastal Buddy Scheme\" \n",
"# \"Coupled with convection\" \n",
"# \"Coupled with gravity waves\" \n",
"# \"Depth capped at cloud base\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.3. Closure Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Boundary layer turbulence scheme closure order*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.closure_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.4. Counter Gradient\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Uses boundary layer turbulence scheme counter gradient*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.counter_gradient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 31. Turbulence Convection --> Deep Convection \n",
"*Properties of the deep convection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 31.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Deep convection scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.2. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Deep convection scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"mass-flux\" \n",
"# \"adjustment\" \n",
"# \"plume ensemble\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.3. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Deep convection scheme method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CAPE\" \n",
"# \"bulk\" \n",
"# \"ensemble\" \n",
"# \"CAPE/WFN based\" \n",
"# \"TKE/CIN based\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.4. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical processes taken into account in the parameterisation of deep convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"vertical momentum transport\" \n",
"# \"convective momentum transport\" \n",
"# \"entrainment\" \n",
"# \"detrainment\" \n",
"# \"penetrative convection\" \n",
"# \"updrafts\" \n",
"# \"downdrafts\" \n",
"# \"radiative effect of anvils\" \n",
"# \"re-evaporation of convective precipitation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.5. Microphysics\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Microphysics scheme for deep convection. Microphysical processes directly control the amount of detrainment of cloud hydrometeor and water vapor from updrafts*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.microphysics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"tuning parameter based\" \n",
"# \"single moment\" \n",
"# \"two moment\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 32. Turbulence Convection --> Shallow Convection \n",
"*Properties of the shallow convection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 32.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Shallow convection scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.2. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *shallow convection scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"mass-flux\" \n",
"# \"cumulus-capped boundary layer\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.3. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *shallow convection scheme method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"same as deep (unified)\" \n",
"# \"included in boundary layer turbulence\" \n",
"# \"separate diagnosis\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.4. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical processes taken into account in the parameterisation of shallow convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"convective momentum transport\" \n",
"# \"entrainment\" \n",
"# \"detrainment\" \n",
"# \"penetrative convection\" \n",
"# \"re-evaporation of convective precipitation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.5. Microphysics\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Microphysics scheme for shallow convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.microphysics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"tuning parameter based\" \n",
"# \"single moment\" \n",
"# \"two moment\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 33. Microphysics Precipitation \n",
"*Large Scale Cloud Microphysics and Precipitation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 33.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of large scale cloud microphysics and precipitation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 34. Microphysics Precipitation --> Large Scale Precipitation \n",
"*Properties of the large scale precipitation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 34.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name of the large scale precipitation parameterisation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 34.2. Hydrometeors\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Precipitating hydrometeors taken into account in the large scale precipitation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.hydrometeors') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"liquid rain\" \n",
"# \"snow\" \n",
"# \"hail\" \n",
"# \"graupel\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 35. Microphysics Precipitation --> Large Scale Cloud Microphysics \n",
"*Properties of the large scale cloud microphysics scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 35.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name of the microphysics parameterisation scheme used for large scale clouds.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 35.2. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Large scale cloud microphysics processes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"mixed phase\" \n",
"# \"cloud droplets\" \n",
"# \"cloud ice\" \n",
"# \"ice nucleation\" \n",
"# \"water vapour deposition\" \n",
"# \"effect of raindrops\" \n",
"# \"effect of snow\" \n",
"# \"effect of graupel\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 36. Cloud Scheme \n",
"*Characteristics of the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 36.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of the atmosphere cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.3. Atmos Coupling\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Atmosphere components that are linked to the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.atmos_coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"atmosphere_radiation\" \n",
"# \"atmosphere_microphysics_precipitation\" \n",
"# \"atmosphere_turbulence_convection\" \n",
"# \"atmosphere_gravity_waves\" \n",
"# \"atmosphere_solar\" \n",
"# \"atmosphere_volcano\" \n",
"# \"atmosphere_cloud_simulator\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.4. Uses Separate Treatment\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Different cloud schemes for the different types of clouds (convective, stratiform and boundary layer)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.uses_separate_treatment') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.5. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Processes included in the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"entrainment\" \n",
"# \"detrainment\" \n",
"# \"bulk cloud\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.6. Prognostic Scheme\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the cloud scheme a prognostic scheme?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.7. Diagnostic Scheme\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the cloud scheme a diagnostic scheme?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.diagnostic_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.8. Prognostic Variables\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *List the prognostic variables used by the cloud scheme, if applicable.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"cloud amount\" \n",
"# \"liquid\" \n",
"# \"ice\" \n",
"# \"rain\" \n",
"# \"snow\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 37. Cloud Scheme --> Optical Cloud Properties \n",
"*Optical cloud properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 37.1. Cloud Overlap Method\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Method for taking into account overlapping of cloud layers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_overlap_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"random\" \n",
"# \"maximum\" \n",
"# \"maximum-random\" \n",
"# \"exponential\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.2. Cloud Inhomogeneity\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Method for taking into account cloud inhomogeneity*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_inhomogeneity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 38. Cloud Scheme --> Sub Grid Scale Water Distribution \n",
"*Sub-grid scale water distribution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 38.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Sub-grid scale water distribution type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 38.2. Function Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Sub-grid scale water distribution function name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 38.3. Function Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Sub-grid scale water distribution function type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 38.4. Convection Coupling\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Sub-grid scale water distribution coupling with convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.convection_coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"coupled with deep\" \n",
"# \"coupled with shallow\" \n",
"# \"not coupled with convection\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 39. Cloud Scheme --> Sub Grid Scale Ice Distribution \n",
"*Sub-grid scale ice distribution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 39.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Sub-grid scale ice distribution type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 39.2. Function Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Sub-grid scale ice distribution function name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 39.3. Function Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Sub-grid scale ice distribution function type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 39.4. Convection Coupling\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Sub-grid scale ice distribution coupling with convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.convection_coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"coupled with deep\" \n",
"# \"coupled with shallow\" \n",
"# \"not coupled with convection\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 40. Observation Simulation \n",
"*Characteristics of observation simulation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 40.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of observation simulator characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 41. Observation Simulation --> Isscp Attributes \n",
"*ISSCP Characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 41.1. Top Height Estimation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Cloud simulator ISSCP top height estimation methodUo*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_estimation_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"no adjustment\" \n",
"# \"IR brightness\" \n",
"# \"visible optical depth\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 41.2. Top Height Direction\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator ISSCP top height direction*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_direction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"lowest altitude level\" \n",
"# \"highest altitude level\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 42. Observation Simulation --> Cosp Attributes \n",
"*CFMIP Observational Simulator Package attributes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 42.1. Run Configuration\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator COSP run configuration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.run_configuration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Inline\" \n",
"# \"Offline\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 42.2. Number Of Grid Points\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Cloud simulator COSP number of grid points*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_grid_points') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 42.3. Number Of Sub Columns\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Cloud simulator COSP number of sub-cloumns used to simulate sub-grid variability*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_sub_columns') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 42.4. Number Of Levels\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Cloud simulator COSP number of levels*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 43. Observation Simulation --> Radar Inputs \n",
"*Characteristics of the cloud radar simulator*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 43.1. Frequency\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Cloud simulator radar frequency (Hz)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.frequency') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 43.2. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator radar type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"surface\" \n",
"# \"space borne\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 43.3. Gas Absorption\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Cloud simulator radar uses gas absorption*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.gas_absorption') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 43.4. Effective Radius\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Cloud simulator radar uses effective radius*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.effective_radius') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 44. Observation Simulation --> Lidar Inputs \n",
"*Characteristics of the cloud lidar simulator*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 44.1. Ice Types\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator lidar ice type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.ice_types') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"ice spheres\" \n",
"# \"ice non-spherical\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 44.2. Overlap\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Cloud simulator lidar overlap*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.overlap') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"max\" \n",
"# \"random\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 45. Gravity Waves \n",
"*Characteristics of the parameterised gravity waves in the atmosphere, whether from orography or other sources.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 45.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of gravity wave parameterisation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 45.2. Sponge Layer\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Sponge layer in the upper levels in order to avoid gravity wave reflection at the top.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.sponge_layer') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Rayleigh friction\" \n",
"# \"Diffusive sponge layer\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 45.3. Background\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Background wave distribution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"continuous spectrum\" \n",
"# \"discrete spectrum\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 45.4. Subgrid Scale Orography\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Subgrid scale orography effects taken into account.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.subgrid_scale_orography') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"effect on drag\" \n",
"# \"effect on lifting\" \n",
"# \"enhanced topography\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 46. Gravity Waves --> Orographic Gravity Waves \n",
"*Gravity waves generated due to the presence of orography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 46.1. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the orographic gravity wave scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.2. Source Mechanisms\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Orographic gravity wave source mechanisms*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.source_mechanisms') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"linear mountain waves\" \n",
"# \"hydraulic jump\" \n",
"# \"envelope orography\" \n",
"# \"low level flow blocking\" \n",
"# \"statistical sub-grid scale variance\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.3. Calculation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Orographic gravity wave calculation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.calculation_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"non-linear calculation\" \n",
"# \"more than two cardinal directions\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.4. Propagation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Orographic gravity wave propogation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.propagation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"linear theory\" \n",
"# \"non-linear theory\" \n",
"# \"includes boundary layer ducting\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.5. Dissipation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Orographic gravity wave dissipation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.dissipation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"total wave\" \n",
"# \"single wave\" \n",
"# \"spectral\" \n",
"# \"linear\" \n",
"# \"wave saturation vs Richardson number\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 47. Gravity Waves --> Non Orographic Gravity Waves \n",
"*Gravity waves generated by non-orographic processes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 47.1. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the non-orographic gravity wave scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.2. Source Mechanisms\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Non-orographic gravity wave source mechanisms*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.source_mechanisms') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"convection\" \n",
"# \"precipitation\" \n",
"# \"background spectrum\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.3. Calculation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Non-orographic gravity wave calculation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.calculation_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"spatially dependent\" \n",
"# \"temporally dependent\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.4. Propagation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Non-orographic gravity wave propogation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.propagation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"linear theory\" \n",
"# \"non-linear theory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.5. Dissipation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Non-orographic gravity wave dissipation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.dissipation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"total wave\" \n",
"# \"single wave\" \n",
"# \"spectral\" \n",
"# \"linear\" \n",
"# \"wave saturation vs Richardson number\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 48. Solar \n",
"*Top of atmosphere solar insolation characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 48.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of solar insolation of the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 49. Solar --> Solar Pathways \n",
"*Pathways for solar forcing of the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 49.1. Pathways\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Pathways for the solar forcing of the atmosphere model domain*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_pathways.pathways') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"SW radiation\" \n",
"# \"precipitating energetic particles\" \n",
"# \"cosmic rays\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 50. Solar --> Solar Constant \n",
"*Solar constant and top of atmosphere insolation characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 50.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time adaptation of the solar constant.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_constant.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed\" \n",
"# \"transient\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 50.2. Fixed Value\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *If the solar constant is fixed, enter the value of the solar constant (W m-2).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_constant.fixed_value') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 50.3. Transient Characteristics\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *solar constant transient characteristics (W m-2)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_constant.transient_characteristics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 51. Solar --> Orbital Parameters \n",
"*Orbital parameters and top of atmosphere insolation characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 51.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time adaptation of orbital parameters*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed\" \n",
"# \"transient\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 51.2. Fixed Reference Date\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Reference date for fixed orbital parameters (yyyy)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.fixed_reference_date') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 51.3. Transient Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Description of transient orbital parameters*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.transient_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 51.4. Computation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method used for computing orbital parameters.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.computation_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Berger 1978\" \n",
"# \"Laskar 2004\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 52. Solar --> Insolation Ozone \n",
"*Impact of solar insolation on stratospheric ozone*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 52.1. Solar Ozone Impact\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does top of atmosphere insolation impact on stratospheric ozone?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.insolation_ozone.solar_ozone_impact') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 53. Volcanos \n",
"*Characteristics of the implementation of volcanoes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 53.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of the implementation of volcanic effects in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.volcanos.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 54. Volcanos --> Volcanoes Treatment \n",
"*Treatment of volcanoes in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 54.1. Volcanoes Implementation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How volcanic effects are modeled in the atmosphere.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.volcanos.volcanoes_treatment.volcanoes_implementation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"high frequency solar constant anomaly\" \n",
"# \"stratospheric aerosols optical thickness\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### \u00a92017 [ES-DOC](https://es-doc.org) \n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.10",
"pygments_lexer": "ipython2",
"codemirror_mode": {
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"name": "ipython"
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} | gpl-3.0 |