upadting figures and cleaning

Author: LauraGomezNavarro

Code/Plotting/fig_05.ipynb

@@ -250,24 +250,6 @@
     "ds_wT_Jun_month]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Deleted particles"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# these are matplotlib.patch.Patch properties\n",
-    "props = dict(boxstyle='round', facecolor='wheat', alpha=.5)\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -289,7 +271,7 @@
     "        lonmax=-21., \n",
     "        latmin=32.5, \n",
     "        latmax=36.5\n",
-    ")#lomin -35 #lomax -18 # lamin 30 # lamax 40"
+    ")"
    ]
   },
   {
@@ -314,116 +296,77 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nbins = (60, 40)\n",
-    "\n",
-    "xmin = subregion.lonmin\n",
-    "xmax = subregion.lonmax\n",
-    "ymin = subregion.latmin\n",
-    "ymax = subregion.latmax\n",
+    "def calc_hist2d(dsp, nbins, nsubregion):\n",
+    "    \"\"\"\n",
+    "    xxxx\n",
+    "    dsp: xarray dataset \n",
+    "    \"\"\"    \n",
+    "    xmin = nsubregion.lonmin\n",
+    "    xmax = nsubregion.lonmax\n",
+    "    ymin = nsubregion.latmin\n",
+    "    ymax = nsubregion.latmax\n",
+    "    \n",
+    "    lon_end_nonans, lat_end_nonans = rem_nans(dsp)\n",
+    "    counts, xedges, yedges = np.histogram2d(lon_end_nonans, lat_end_nonans, bins=nbins, range=((xmin, xmax), (ymin, ymax)))\n",
     "\n",
-    "vmin=0.\n",
-    "vmax=30."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Histogram 1D"
+    "    return counts.ravel()\n",
+    "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "ymax_1d = .2\n",
-    "xmax_1d = 90\n",
-    "\n",
-    "nbins_1d = 20"
+    "bins_2d = (60, 40) # --> 0.1 degree"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_hist1d_particles_b(dsp, axn, nbins_1d, ntitle, labelf, ncolor='b'):\n",
-    "    \"\"\"\n",
-    "    \"\"\"    \n",
-    "    ######\n",
-    "    # Title\n",
-    "    if ntitle in ntitles[0:6]:\n",
-    "        nyear = '2009'\n",
-    "    elif ntitle in ntitles[6::]:\n",
-    "        nyear = '2010'\n",
-    "    else:\n",
-    "        print('Error nyear')\n",
-    "        dgdsgsg\n",
-    "        \n",
-    "    strtitle = ntitle + ' ' + nyear\n",
-    "    axn.set_title(strtitle, size=16)\n",
-    "    ######\n",
-    "    \n",
-    "    lon_end_nonans, lat_end_nonans = rem_nans(dsp)\n",
-    "    counts, xedges, yedges = np.histogram2d(lon_end_nonans, lat_end_nonans, bins=nbins, range=((xmin, xmax), (ymin, ymax)))\n",
+    "counts_nT = np.ones((12, 60*40)) * np.nan\n",
+    "counts_wT = np.ones((12, 60*40)) * np.nan\n",
     "\n",
-    "    axn.hist(counts.ravel(), bins=nbins_1d, density=True, color=ncolor, alpha=.5, label=labelf)\n",
-    "    axn.set_ylim([0, ymax_1d])\n",
-    "    axn.set_xlim([0, xmax_1d])\n",
-    "    axn.grid()\n",
-    "    axn.axvline(x=counts.ravel().max(), color=ncolor)\n",
-    "    #print(counts.ravel().max())\n",
-    "    skw_2D = scipy.stats.skew(counts.ravel())\n",
-    "    #     axn.annotate('Skewness = ' + str(np.round(scipy.stats.skew(counts.ravel()), 2)),\n",
-    "    #                 xy=(22, .17), xycoords='data', size=16)\n",
-    "    return skw_2D, counts.ravel()\n",
-    "    "
+    "for ii in range(0, len(ntitles)):\n",
+    "    counts_nT[ii,:] = calc_hist2d(dsps_nT[ii], bins_2d, subregion)\n",
+    "    counts_wT[ii,:] = calc_hist2d(dsps_wT[ii], bins_2d, subregion)\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 18,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#https://matplotlib.org/3.3.4/gallery/recipes/placing_text_boxes.html\n",
-    "\n",
-    "# these are matplotlib.patch.Patch properties\n",
-    "props = dict(boxstyle='round', facecolor='wheat', alpha=1) #  alpha=\n"
+    "# Histogram 1D"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Defining same binsize for all months instead of bin number"
+    "## Doing it with number of bins:"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 19,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "ymax_1d = .15\n",
-    "\n",
-    "bins_list = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
-    "# specify bin start and end points"
+    "* 10 max 70:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "ymax_1d = .15\n",
+    "bins_list = 10\n",
     "\n",
-    "bins_list = [0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90]\n",
-    "# specify bin start and end points"
+    "ymax_1d = 0.185\n",
+    "xmax_1d = 77"
    ]
   },
   {
@@ -445,8 +388,6 @@
     }
    ],
    "source": [
-    "xmax_1d = 80\n",
-    "\n",
     "fig = plt.figure(figsize=(16,13)) #(17,5)\n",
     "\n",
     "#cbar_ticks = [0.005, 0.0055, 0.006, 0.0065, 0.007, 0.0075, 0.008, 0.0085, 0.009] \n",
@@ -472,23 +413,31 @@
     "    strtitle = ntitle + '. ' + nyear\n",
     "    ax1.set_title(strtitle, size=16)\n",
     "    ######\n",
+    "       \n",
+    "    counts_nT_1, binEdges_nT = np.histogram(counts_nT[ii,:], bins=bins_list, density=True)\n",
+    "    counts_wT_1, binEdges_wT = np.histogram(counts_wT[ii,:], bins=bins_list, density=True)\n",
+    "        \n",
+    "    bincenters_nT = 0.5 * (binEdges_nT[1:] + binEdges_nT[:-1])\n",
+    "    bincenters_wT = 0.5 * (binEdges_wT[1:] + binEdges_wT[:-1])\n",
     "    \n",
-    "    ax1.hist(counts_nT[ii,:], bins=bins_list, density=True, alpha=.5, label='No tidal forcing', color='b')\n",
+    "    ax1.plot(bincenters_nT, counts_nT_1, '.-', color='C0', label='No tidal forcing', linewidth=2, markersize=10)# , linewidth=4\n",
     "    ax1.set_ylim([0, ymax_1d])\n",
     "    ax1.set_xlim([0, xmax_1d])\n",
-    "    ax1.axvline(x=counts_nT[ii,:].max(), color='b')\n",
+    "    ax1.axvline(x=counts_nT[ii,:].max(), linestyle='--', color='C0')\n",
     "    \n",
-    "    ax1.hist(counts_wT[ii,:], bins=bins_list, density=True, alpha=.5, label='Tidal forcing', color='r')\n",
+    "    ax1.plot(bincenters_wT, counts_wT_1, '.-', color='C3', label='Tidal forcing', linewidth=2, markersize=10)# , linewidth=4\n",
     "    ax1.set_ylim([0, ymax_1d])\n",
     "    ax1.set_xlim([0, xmax_1d])\n",
-    "    ax1.axvline(x=counts_wT[ii,:].max(), color='r')\n",
+    "    ax1.axvline(x=counts_wT[ii,:].max(), linestyle='--', color='C3')\n",
     "    \n",
     "    #skw_2D_wT, _ = plot_hist1d_particles_b(dsps_wT[ii], ax1, bins_list, ntitles[ii], labelf='Tidal', ncolor='r')\n",
     "    ax1.grid()\n",
     "    ax1.tick_params(axis='both', which='major', labelsize=14)\n",
     "\n",
     "    if ii == 0:\n",
-    "        ax1.legend(loc=0, fontsize=14)\n",
+    "        legend = ax1.legend(loc=0, fontsize=12)\n",
+    "        legend.get_frame().set_alpha(None)\n",
+    "        legend.get_frame().set_facecolor('white')\n",
     "    if ((ii==4)):\n",
     "        ax1.set_ylabel('Normalized frequency', size=20) # Normalized bin counts\n",
     "    if ((ii==9)):\n",
@@ -497,14 +446,8 @@
     "        ax1.axes.xaxis.set_ticklabels([])\n",
     "    if (ii != 0) & (ii != 4) & (ii != 8) :\n",
     "        ax1.axes.yaxis.set_ticklabels([])\n",
-    "#     textstr = '\\n'.join((\n",
-    "#     r'$nT=%.2f$' % (skw_2D_nT, ),\n",
-    "#     r'$wT=%.2f$' % (skw_2D_wT, )))\n",
-    "#     # place a text box in upper left in axes coords\n",
-    "#     ax1.text(0.55, 0.95, textstr, transform=ax1.transAxes, fontsize=14,\n",
-    "#         verticalalignment='top', bbox=props)\n",
-    "\n",
-    "plt.savefig('figs/' + 'hist1d_nT_wT_monthly_nobox.jpg', dpi=300)\n",
+    "        \n",
+    "plt.savefig('../../Figures/' + 'hist1d_nT_wT_monthly_nobox.jpg', dpi=300)\n",
     "  \n",
     "plt.show()\n"
    ]
@@ -535,7 +478,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.11"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,