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243 lines
6.2 KiB
Plaintext
243 lines
6.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Profile picker"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true
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},
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"source": [
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"## Setup notebook"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true
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},
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"outputs": [],
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"source": [
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"# Enable autoreloading of our modules. \n",
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"# Most of the code will be located in the /src/ folder, \n",
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"# and then called from the notebook.\n",
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"%matplotlib inline\n",
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"%reload_ext autoreload\n",
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"%autoreload"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true
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},
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"outputs": [],
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"source": [
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"from IPython.core.debugger import set_trace\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"import decimal\n",
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"import plotly\n",
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"import plotly.graph_objs as go\n",
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"import plotly.plotly as py\n",
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"import plotly.tools as tls\n",
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"import plotly.figure_factory as ff\n",
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"from plotly import tools\n",
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"import plotly.io as pio\n",
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"from scipy import stats\n",
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"import math\n",
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"import matplotlib\n",
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"from matplotlib import cm\n",
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"import colorlover as cl\n",
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"import numpy.ma as ma\n",
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"\n",
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"from ipywidgets import widgets, Output\n",
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"from IPython.display import display, clear_output, Image, HTML\n",
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"\n",
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"from sklearn.metrics import confusion_matrix\n",
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"\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"from sklearn import linear_model, datasets\n",
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"\n",
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"from scipy.interpolate import UnivariateSpline\n",
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"from scipy.interpolate import interp1d\n",
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"from scipy.interpolate import splrep, splev\n",
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"from scipy.integrate import simps\n",
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"from scipy.stats import linregress\n",
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"from scipy.signal import find_peaks\n",
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"import json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true
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},
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"outputs": [],
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"source": [
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"# Matplot lib default settings\n",
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"plt.rcParams[\"figure.figsize\"] = (10,6)\n",
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"plt.rcParams['axes.grid']=True\n",
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"plt.rcParams['grid.alpha'] = 0.5\n",
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"plt.rcParams['grid.color'] = \"grey\"\n",
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"plt.rcParams['grid.linestyle'] = \"--\"\n",
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"plt.rcParams['axes.grid']=True\n",
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"\n",
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"# https://stackoverflow.com/a/20709149\n",
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"matplotlib.rcParams['text.usetex'] = True\n",
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"\n",
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"matplotlib.rcParams['text.latex.preamble'] = [\n",
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" r'\\usepackage{siunitx}', # i need upright \\micro symbols, but you need...\n",
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" r'\\sisetup{detect-all}', # ...this to force siunitx to actually use your fonts\n",
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" r'\\usepackage{helvet}', # set the normal font here\n",
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" r'\\usepackage{amsmath}',\n",
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" r'\\usepackage{sansmath}', # load up the sansmath so that math -> helvet\n",
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" r'\\sansmath', # <- tricky! -- gotta actually tell tex to use!\n",
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"] "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import data\n",
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"Let's first import data from our pre-processed interim data folder."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
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" print('Importing {}'.format(csv))\n",
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" return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
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"\n",
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"df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
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"df_profile_features_crest_toes = df_from_csv('profile_features_crest_toes.csv', index_col=[0,1])\n",
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"\n",
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"print('Done!')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Manually pick features"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib notebook\n",
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"\n",
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"sites = df_profiles.index.get_level_values('site_id').unique()\n",
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"\n",
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"\n",
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"fig =plt.figure(figsize=(10, 3))\n",
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"\n",
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"df_prestorm = df_profiles.xs((sites[0],'prestorm'),level=('site_id','profile_type'))\n",
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"df_poststorm = df_profiles.xs((sites[0],'poststorm'),level=('site_id','profile_type'))\n",
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"line_prestorm, = plt.plot(df_prestorm.index, df_prestorm.z, label='prestorm')\n",
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"line_poststorm, = plt.plot(df_prestorm.index, df_prestorm.z, label='poststorm')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# df_profiles.xs((sites[0],'prestorm'),level=('site_id','profile_type'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"hide_input": false,
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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