environment.yml

name: accra_wkshp_ucl
channels:
    - conda-forge
dependencies:
    - jupyterlab
    - notebook
    - ipykernel
    - ipywidgets
    - scikit-learn
    - widgetsnbextension
    - matplotlib
    - pandas
    - scipy
    - python>=3.6
    - jupyter_contrib_nbextensions
    - widgetsnbextension
    - pip

matplotlibrc

### MATPLOTLIBRC FORMAT

# This is a sample matplotlib configuration file - you can find a copy
# of it on your system in
# site-packages/matplotlib/mpl-data/matplotlibrc.  If you edit it
# there, please note that it will be overridden in your next install.
# If you want to keep a permanent local copy that will not be
# over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux
# like systems) and C:\Documents and Settings\yourname\.matplotlib
# (win32 systems).
#
# This file is best viewed in a editor which supports python mode
# syntax highlighting. Blank lines, or lines starting with a comment
# symbol, are ignored, as are trailing comments.  Other lines must
# have the format
#    key : val # optional comment
#
# Colors: for the color values below, you can either use - a
# matplotlib color string, such as r, k, or b - an rgb tuple, such as
# (1.0, 0.5, 0.0) - a hex string, such as ff00ff or #ff00ff - a scalar
# grayscale intensity such as 0.75 - a legal html color name, eg red,
# blue, darkslategray

#### CONFIGURATION BEGINS HERE

# the default backend; one of GTK GTKAgg GTKCairo CocoaAgg FltkAgg
# MacOSX QtAgg Qt4Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG Template
# You can also deploy your own backend outside of matplotlib by
# referring to the module name (which must be in the PYTHONPATH) as
# 'module://my_backend'
backend      : Qt5Agg

# if you are runing pyplot inside a GUI and your backend choice
# conflicts, we will automatically try and find a compatible one for
# you if backend_fallback is True
#backend_fallback: True
interactive  : True
toolbar      : toolbar2   # None | classic | toolbar2
timezone     : UTC        # a pytz timezone string, eg US/Central or Europe/Paris

# Where your matplotlib data lives if you installed to a non-default
# location.  This is where the matplotlib fonts, bitmaps, etc reside
#datapath : /home/jdhunter/mpldata


### LINES
# See http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.lines for more
# information on line properties.
lines.linewidth   : 2.0     # line width in points
#lines.linestyle   : -       # solid line
# lines.color       : purple
#lines.marker      : None    # the default marker
#lines.markeredgewidth  : 0.5     # the line width around the marker symbol
#lines.markersize  : 6            # markersize, in points
#lines.dash_joinstyle : miter        # miter|round|bevel
#lines.dash_capstyle : butt          # butt|round|projecting
#lines.solid_joinstyle : miter       # miter|round|bevel
#lines.solid_capstyle : projecting   # butt|round|projecting
lines.antialiased : True         # render lines in antialised (no jaggies)

### PATCHES
# Patches are graphical objects that fill 2D space, like polygons or
# circles.  See
# http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.patches
# information on patch properties
patch.linewidth        : 0.5     # edge width in points
patch.facecolor        : 348ABD # blue
patch.edgecolor        : eeeeee
patch.antialiased      : True    # render patches in antialised (no jaggies)

### FONT
#
# font properties used by text.Text.  See
# http://matplotlib.sourceforge.net/api/font_manager_api.html for more
# information on font properties.  The 6 font properties used for font
# matching are given below with their default values.
#
# The font.family property has five values: 'serif' (e.g. Times),
# 'sans-serif' (e.g. Helvetica), 'cursive' (e.g. Zapf-Chancery),
# 'fantasy' (e.g. Western), and 'monospace' (e.g. Courier).  Each of
# these font families has a default list of font names in decreasing
# order of priority associated with them.
#
# The font.style property has three values: normal (or roman), italic
# or oblique.  The oblique style will be used for italic, if it is not
# present.
#
# The font.variant property has two values: normal or small-caps.  For
# TrueType fonts, which are scalable fonts, small-caps is equivalent
# to using a font size of 'smaller', or about 83% of the current font
# size.
#
# The font.weight property has effectively 13 values: normal, bold,
# bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as
# 400, and bold is 700.  bolder and lighter are relative values with
# respect to the current weight.
#
# The font.stretch property has 11 values: ultra-condensed,
# extra-condensed, condensed, semi-condensed, normal, semi-expanded,
# expanded, extra-expanded, ultra-expanded, wider, and narrower.  This
# property is not currently implemented.
#
# The font.size property is the default font size for text, given in pts.
# 12pt is the standard value.
#
font.family          : sans-serif
#font.style          : normal
#font.variant        : normal
#font.weight         : medium
#font.stretch        : normal
# note that font.size controls default text sizes.  To configure
# special text sizes tick labels, axes, labels, title, etc, see the rc
# settings for axes and ticks. Special text sizes can be defined
# relative to font.size, using the following values: xx-small, x-small,
# small, medium, large, x-large, xx-large, larger, or smaller
font.size           : 12.0
# font.serif          : Bitstream Vera Serif, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif
font.sans-serif     : Helvetica
#font.cursive        : Apple Chancery, Textile, Zapf Chancery, Sand, cursive
#font.fantasy        : Comic Sans MS, Chicago, Charcoal, Impact, Western, fantasy
font.monospace      : Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace

### TEXT
# text properties used by text.Text.  See
# http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.text for more
# information on text properties

#text.color          : black

### LaTeX customizations. See http://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex
#text.usetex         : False  # use latex for all text handling. The following fonts
                              # are supported through the usual rc parameter settings:
                              # new century schoolbook, bookman, times, palatino,
                              # zapf chancery, charter, serif, sans-serif, helvetica,
                              # avant garde, courier, monospace, computer modern roman,
                              # computer modern sans serif, computer modern typewriter
                              # If another font is desired which can loaded using the
                              # LaTeX \usepackage command, please inquire at the
                              # matplotlib mailing list
#text.latex.unicode : False # use "ucs" and "inputenc" LaTeX packages for handling
                            # unicode strings.
#text.latex.preamble :  # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES
                            # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP
                            # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.
                            # preamble is a comma separated list of LaTeX statements
                            # that are included in the LaTeX document preamble.
                            # An example:
                            # text.latex.preamble : \usepackage{bm},\usepackage{euler}
                            # The following packages are always loaded with usetex, so
                            # beware of package collisions: color, geometry, graphicx,
                            # type1cm, textcomp. Adobe Postscript (PSSNFS) font packages
                            # may also be loaded, depending on your font settings

#text.dvipnghack : None      # some versions of dvipng don't handle alpha
                             # channel properly.  Use True to correct
                             # and flush ~/.matplotlib/tex.cache
                             # before testing and False to force
                             # correction off.  None will try and
                             # guess based on your dvipng version

#text.markup         : 'plain'  # Affects how text, such as titles and labels, are
                                # interpreted by default.
                                # 'plain': As plain, unformatted text
                                # 'tex': As TeX-like text.  Text between $'s will be
                                # formatted as a TeX math expression.
                                # This setting has no effect when text.usetex is True.
                                # In that case, all text will be sent to TeX for
                                # processing.

#text.hinting : True # If True, text will be hinted, otherwise not.  This only
                     # affects the Agg backend.

# The following settings allow you to select the fonts in math mode.
# They map from a TeX font name to a fontconfig font pattern.
# These settings are only used if mathtext.fontset is 'custom'.
# Note that this "custom" mode is unsupported and may go away in the
# future.
#mathtext.cal : cursive
#mathtext.rm  : serif
#mathtext.tt  : monospace
#mathtext.it  : serif:italic
#mathtext.bf  : serif:bold
#mathtext.sf  : sans
#mathtext.fontset : cm # Should be 'cm' (Computer Modern), 'stix',
                       # 'stixsans' or 'custom'
#mathtext.fallback_to_cm : True  # When True, use symbols from the Computer Modern
                                 # fonts when a symbol can not be found in one of
                                 # the custom math fonts.

#mathtext.default : it # The default font to use for math.
                       # Can be any of the LaTeX font names, including
                       # the special name "regular" for the same font
                       # used in regular text.

### AXES
# default face and edge color, default tick sizes,
# default fontsizes for ticklabels, and so on.  See
# http://matplotlib.sourceforge.net/api/axes_api.html#module-matplotlib.axes
#axes.hold           : True    # whether to clear the axes by default on
axes.facecolor      : w        # axes background color
axes.edgecolor      : 262626   # axes edge color
axes.linewidth      : 1        # edge linewidth
axes.grid           : False     # display grid or not
axes.titlesize      : x-large  # fontsize of the axes title
axes.labelsize      : large    # fontsize of the x any y labels
axes.labelcolor     : 262626
axes.axisbelow      : True     # whether axis gridlines and ticks are below
                               # the axes elements (lines, text, etc)
axes.formatter.limits : -4, 4 # use scientific notation if log10
                               # of the axis range is smaller than the
                               # first or larger than the second
#axes.unicode_minus  : True    # use unicode for the minus symbol
                               # rather than hypen.  See http://en.wikipedia.org/wiki/Plus_sign#Plus_sign
axes.prop_cycle    : cycler("color", ["FC8D62", "66C2A5", "8DA0CB", "E78AC3", "A6D854", "FFD92F", "E5C494", "B3B3B3"])
                      # E24A33 : orange
                      # 7A68A6 : purple
                      # 348ABD : blue
                      # 188487 : turquoise
                      # A60628 : red
                      # CF4457 : pink
                      # 467821 : green

axes.spines.right : False
axes.spines.top : False
                                            
                                            
                                            
                                            # color cycle for plot lines
                                            # as list of string colorspecs:
                                            # single letter, long name, or
                                            # web-style hex

#polaraxes.grid      : True    # display grid on polar axes
#axes3d.grid         : True    # display grid on 3d axes

### TICKS
# see http://matplotlib.sourceforge.net/api/axis_api.html#matplotlib.axis.Tick
xtick.major.size     : 10      # major tick size in points
xtick.major.width     : 0.5      # major tick size in points
xtick.minor.size     : 10      # minor tick size in points
xtick.minor.width     : 0.5      # major tick size in points
xtick.major.pad      : 6      # distance to major tick label in points
xtick.minor.pad      : 6      # distance to the minor tick label in points
xtick.color          : 262626      # color of the tick labels
#xtick.labelsize      : 12 # fontsize of the tick labels
xtick.direction      : out     # direction: in or out

ytick.major.size     : 10      # major tick size in points
ytick.major.width     : 0.5      # major tick size in points
ytick.minor.size     : 10      # minor tick size in points
ytick.minor.width     : 0.5      # major tick size in points
ytick.major.pad      : 6      # distance to major tick label in points
ytick.minor.pad      : 6      # distance to the minor tick label in points
ytick.color          : 262626      # color of the tick labels
#ytick.labelsize      : 12 # fontsize of the tick labels
ytick.direction      : out     # direction: in or out


### GRIDS
#grid.color       :   black   # grid color
#grid.linestyle   :   :       # dotted
#grid.linewidth   :   0.5     # in points

### Legend
legend.fancybox      : True  # if True, use a rounded box for the
                               # legend, else a rectangle
#legend.isaxes        : True
legend.numpoints     : 1      # the number of points in the legend line
#legend.fontsize      : large
#legend.pad           : 0.0    # deprecated; the fractional whitespace inside the legend border
#legend.borderpad     : 0.5    # border whitspace in fontsize units
#legend.markerscale   : 1.0    # the relative size of legend markers vs. original
# the following dimensions are in axes coords
#legend.labelsep      : 0.010  # the vertical space between the legend entries
#legend.handlelen     : 0.05   # the length of the legend lines
#legend.handletextsep : 0.02   # the space between the legend line and legend text
#legend.axespad       : 0.02   # the border between the axes and legend edge
#legend.shadow        : False

### FIGURE
# See http://matplotlib.sourceforge.net/api/figure_api.html#matplotlib.figure.Figure
figure.figsize   : 6, 6    # figure size in inches
#figure.dpi       : 80      # figure dots per inch
figure.facecolor : 0.85    # figure facecolor; 0.75 is scalar gray
figure.edgecolor : 0.50   # figure edgecolor

# The figure subplot parameters.  All dimensions are fraction of the
# figure width or height
#figure.subplot.left    : 0.125  # the left side of the subplots of the figure
#figure.subplot.right   : 0.9    # the right side of the subplots of the figure
#figure.subplot.bottom  : 0.1    # the bottom of the subplots of the figure
#figure.subplot.top     : 0.9    # the top of the subplots of the figure
#figure.subplot.wspace  : 0.2    # the amount of width reserved for blank space between subplots
figure.subplot.hspace  : 0.5    # the amount of height reserved for white space between subplots

### IMAGES
#image.aspect : equal             # equal | auto | a number
#image.interpolation  : bilinear  # see help(imshow) for options
#image.cmap   : jet               # gray | jet etc...
#image.lut    : 256               # the size of the colormap lookup table
#image.origin : upper             # lower | upper
#image.resample  : False

### CONTOUR PLOTS
#contour.negative_linestyle :  dashed # dashed | solid

### Agg rendering
### Warning: experimental, 2008/10/10
#agg.path.chunksize : 0           # 0 to disable; values in the range
                                  # 10000 to 100000 can improve speed slightly
                                  # and prevent an Agg rendering failure
                                  # when plotting very large data sets,
                                  # especially if they are very gappy.
                                  # It may cause minor artifacts, though.
                                  # A value of 20000 is probably a good
                                  # starting point.
### SAVING FIGURES
#path.simplify : True   # When True, simplify paths by removing "invisible"
                        # points to reduce file size and increase rendering
                        # speed
#path.simplify_threshold : 0.1  # The threshold of similarity below which
                                # vertices will be removed in the simplification
                                # process
#path.snap : True # When True, rectilinear axis-aligned paths will be snapped to
                  # the nearest pixel when certain criteria are met.  When False,
                  # paths will never be snapped.

# the default savefig params can be different from the display params
# Eg, you may want a higher resolution, or to make the figure
# background white
#savefig.dpi       : 100      # figure dots per inch
#savefig.facecolor : white    # figure facecolor when saving
#savefig.edgecolor : white    # figure edgecolor when saving
#savefig.extension : auto     # what extension to use for savefig('foo'), or 'auto'

#cairo.format      : png      # png, ps, pdf, svg

# tk backend params
#tk.window_focus   : False    # Maintain shell focus for TkAgg

# ps backend params
#ps.papersize      : letter   # auto, letter, legal, ledger, A0-A10, B0-B10
#ps.useafm         : False    # use of afm fonts, results in small files
#ps.usedistiller   : False    # can be: None, ghostscript or xpdf
                                          # Experimental: may produce smaller files.
                                          # xpdf intended for production of publication quality files,
                                          # but requires ghostscript, xpdf and ps2eps
#ps.distiller.res  : 6000      # dpi
#ps.fonttype       : 3         # Output Type 3 (Type3) or Type 42 (TrueType)

# pdf backend params
#pdf.compression   : 6 # integer from 0 to 9
                       # 0 disables compression (good for debugging)
#pdf.fonttype       : 3         # Output Type 3 (Type3) or Type 42 (TrueType)

# svg backend params
#svg.image_inline : True       # write raster image data directly into the svg file
#svg.image_noscale : False     # suppress scaling of raster data embedded in SVG
#svg.embed_char_paths : True       # embed character outlines in the SVG file

# docstring params
#docstring.hardcopy = False  # set this when you want to generate hardcopy docstring

# Set the verbose flags.  This controls how much information
# matplotlib gives you at runtime and where it goes.  The verbosity
# levels are: silent, helpful, debug, debug-annoying.  Any level is
# inclusive of all the levels below it.  If your setting is "debug",
# you'll get all the debug and helpful messages.  When submitting
# problems to the mailing-list, please set verbose to "helpful" or "debug"
# and paste the output into your report.
#
# The "fileo" gives the destination for any calls to verbose.report.
# These objects can a filename, or a filehandle like sys.stdout.
#
# You can override the rc default verbosity from the command line by
# giving the flags --verbose-LEVEL where LEVEL is one of the legal
# levels, eg --verbose-helpful.
#
# You can access the verbose instance in your code
#   from matplotlib import verbose.
#verbose.level  : silent      # one of silent, helpful, debug, debug-annoying
#verbose.fileo  : sys.stdout  # a log filename, sys.stdout or sys.stderr

# Event keys to interact with figures/plots via keyboard.
# Customize these settings according to your needs.
# Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen : '')

keymap.fullscreen : f               # toggling
keymap.home : h, r, home            # home or reset mnemonic
keymap.back : left, c, backspace    # forward / backward keys to enable
keymap.forward : right, v           #   left handed quick navigation
keymap.pan : p                      # pan mnemonic
keymap.zoom : o                     # zoom mnemonic
keymap.save : s                     # saving current figure
keymap.grid : g                     # switching on/off a grid in current axes
keymap.yscale : l                   # toggle scaling of y-axes ('log'/'linear')
keymap.xscale : L, k                # toggle scaling of x-axes ('log'/'linear')
keymap.all_axes : a                 # enable all axes

postBuild

#!/bin/bash

# Download netcdf files
jupyter contrib nbextension install --user
jupyter nbextension enable --py widgetsnbextension
jupyter nbextension enable python-markdown/main

# Install a JupyterLab extension for demonstration purposes
jupyter labextension install @jupyterlab/geojson-extension 
jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter trust *.ipynb

RunMe.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python3.7/site-packages/ipykernel_launcher.py:2: MatplotlibDeprecationWarning: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import sklearn.linear_model as linear_model\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fourier_matrix(x, n_harms=50, N=365):\n",
    "    \"\"\"Generates a matrix with the DCT terms\"\"\"\n",
    "    F = [np.cos(n*np.pi/N*x) if n % 2== 0\n",
    "         else np.cos(n*np.pi/N*x) for n in range(n_harms)]\n",
    "    return np.array(F)\n",
    "\n",
    "\n",
    "\n",
    "def fit_harms_lasso(x, A, gamma=0.005):\n",
    "    \"\"\"Fits observations x using A matrix with gamma value.\n",
    "    It is assumed missing observations are given by a NaN.\n",
    "    \"\"\"\n",
    "    \n",
    "    if np.sum(np.isfinite(x)) == 0:\n",
    "        return np.ones(10)*np.nan\n",
    "    missing = np.isnan(x)\n",
    "    F = A*1.\n",
    "    A = A[:, ~missing]\n",
    "    clf = linear_model.Lasso(alpha=gamma)\n",
    "    clf.fit(A.T, x[~missing])\n",
    "    coefs = clf.coef_\n",
    "    rmse = (clf.predict(A.T) - x[~missing]).std()\n",
    "    return coefs, clf.predict(F.T), rmse\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88780a83115b47d6a75de9899a9d918f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='fname', options=('tseries1.npz', 'tseries2.npz', 'tseries3.npz'), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(fname=[\"tseries1.npz\", \"tseries2.npz\", \"tseries3.npz\"], gamma=widgets.FloatLogSlider(\n",
    "    value=0.005,\n",
    "    min=-4,  \n",
    "    max=-1, \n",
    "    description='Gamma'\n",
    "))\n",
    "def run_me(fname, gamma):\n",
    "    f = np.load(fname)\n",
    "    doys = f[\"doys\"]\n",
    "    xx = f[\"ndvi\"]\n",
    "    missing = np.isnan(xx)\n",
    "    fig, axs = plt.subplots(nrows=6, ncols=3, figsize=(12, 12))\n",
    "\n",
    "    for i, n_harms in enumerate([3, 5, 7, 10, 15, 20]):\n",
    "        F = fourier_matrix(doys,n_harms=n_harms)\n",
    "        coefs, y_pred, rmse = fit_harms_lasso(xx, F, gamma=gamma)\n",
    "        axs[i][0].plot(doys, y_pred)\n",
    "        axs[i][0].plot(doys, xx, 'o')\n",
    "        axs[i][0].set_ylim(0,1)\n",
    "        axs[i][0].set_title(f\"N Harms: {n_harms} len(xx):{(~missing).sum()}\" + \n",
    "                            \" $\\gamma$=\" + \n",
    "                            f\"{gamma}\")\n",
    "\n",
    "        axs[i][1].plot(doys[~missing], xx[~missing] -y_pred[~missing], '+')\n",
    "        axs[i][1].axhline(y=0, lw=0.5, c=\"0.8\")\n",
    "        axs[i][1].set_title(f\"RMSE: {rmse}\")\n",
    "\n",
    "        axs[i][2].bar(np.arange(1, len(coefs)+1), coefs)\n",
    "        axs[i][2].set_ylabel(f\"N freqs {n_harms}\")\n",
    "        axs[i][2].set_title(f\"N coeffs: {np.sum(np.abs(coefs)>1e-5)}\")\n",
    "        axs[i][2].set_xlim(0, 20)\n",
    "    fig.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

tseries1.npz

tseries2.npz

tseries3.npz