reorg imports

clean_plot/plot/utils.py

@@ -3,10 +3,9 @@
 __all__ = ['Plot']
 
 # Cell
-from fastcore.basics import store_attr
+from fastcore.basics import store_attr, patch_to, patch
 from fastcore.xtras import globtastic
 from fastcore.meta import delegates
-from fastcore.basics import patch_to, patch
 from pathlib import Path
 import os
 import numpy as np
@@ -53,16 +52,48 @@ def create_ssms(self):
             del norm_ssm
 
 
-    def get_standardized(self):
+    def get_standardized(self, start, end):
         pass
 
+
     def get_corr_plots(self):
         pass
 
-    def get_sectional_ssms(self):
-        pass
+    def get_sectional_ssms(self, start, end):
+        import gc
+        if start == 0 and end == -1:
+            pass
+        else:
+            assert start < end, 'Incorrect bounds'
+        new_path = self.path/f'sections_{start} {end}'
+        new_path.mkdir(exist_ok=True)
+
+        if start == 0:
+            labels = np.linspace(start + 1, end, y, dtype=int)
+        else:
+            labels = np.linspace(start, end, y, dtype=int)
+
+        ticks = np.linspace(1, end - start, y, dtype=int)
+
+        for method, norm_ssm in self.norm.items():
+            title = f'{self.book_name} {method}'
+            sns.heatmap(norm_ssm[start:end, start:end], cmap='hot',
+                        vmin=0, vmax=1, square=True,
+                        xticklabels=False)
+            length = norm_ssm.shape[0]
+
+
+
+            plt.yticks(ticks, ticks, rotation = 0)
+            plt.ylabel('sentence number')
+            plt.savefig(new_path/f'{title}.png', dpi = 300, bbox_inches='tight')
+            print(f'Done plotting {title}.png')
+            plt.clf()
+            del norm_ssm
+            _ = gc.collect()
 
     def __repr__(self):
+        # remember __str__ calls the __repr_ internally
         dir_path = os.path.dirname(os.path.realpath(self.path))
         return f'This object contains the path to `{dir_path}`'
 
@@ -71,6 +102,7 @@ def __repr__(self):
 def get_normalized(self:Plot):
     "Returns the normalized ssms"
     files = self.view_all_files(file_glob='*.npy')
+
     for f in files:
         f = Path(f)
         fname = f.stem.split('_cleaned_')

nbs/05_plot.utils.ipynb

@@ -19,6 +19,17 @@
     "> This module tries to include most of the plotting functionality available in the package"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a11a137",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -27,10 +38,9 @@
    "outputs": [],
    "source": [
     "#|export\n",
-    "from fastcore.basics import store_attr\n",
+    "from fastcore.basics import store_attr, patch_to, patch\n",
     "from fastcore.xtras import globtastic\n",
     "from fastcore.meta import delegates\n",
-    "from fastcore.basics import patch_to, patch\n",
     "from pathlib import Path\n",
     "import os \n",
     "import numpy as np\n",
@@ -101,16 +111,48 @@
     "            del norm_ssm\n",
     "            \n",
     "    \n",
-    "    def get_standardized(self):\n",
+    "    def get_standardized(self, start, end):\n",
     "        pass\n",
+    "        \n",
     "    \n",
     "    def get_corr_plots(self):\n",
     "        pass\n",
     "    \n",
-    "    def get_sectional_ssms(self):\n",
-    "        pass\n",
+    "    def get_sectional_ssms(self, start, end):\n",
+    "        import gc\n",
+    "        if start == 0 and end == -1:\n",
+    "            pass\n",
+    "        else:\n",
+    "            assert start < end, 'Incorrect bounds'\n",
+    "        new_path = self.path/f'sections_{start} {end}'\n",
+    "        new_path.mkdir(exist_ok=True)\n",
+    "        \n",
+    "        if start == 0:\n",
+    "            labels = np.linspace(start + 1, end, y, dtype=int)\n",
+    "        else:\n",
+    "            labels = np.linspace(start, end, y, dtype=int)\n",
+    "        \n",
+    "        ticks = np.linspace(1, end - start, y, dtype=int)\n",
+    "        \n",
+    "        for method, norm_ssm in self.norm.items():\n",
+    "            title = f'{self.book_name} {method}'\n",
+    "            sns.heatmap(norm_ssm[start:end, start:end], cmap='hot', \n",
+    "                        vmin=0, vmax=1, square=True, \n",
+    "                        xticklabels=False)\n",
+    "            length = norm_ssm.shape[0]\n",
+    "            \n",
+    "            \n",
+    "            \n",
+    "            plt.yticks(ticks, ticks, rotation = 0)\n",
+    "            plt.ylabel('sentence number')\n",
+    "            plt.savefig(new_path/f'{title}.png', dpi = 300, bbox_inches='tight')\n",
+    "            print(f'Done plotting {title}.png')\n",
+    "            plt.clf()\n",
+    "            del norm_ssm\n",
+    "            _ = gc.collect()\n",
     "    \n",
     "    def __repr__(self):\n",
+    "        # remember __str__ calls the __repr_ internally\n",
     "        dir_path = os.path.dirname(os.path.realpath(self.path))\n",
     "        return f'This object contains the path to `{dir_path}`'"
    ]
@@ -127,6 +169,7 @@
     "def get_normalized(self:Plot):\n",
     "    \"Returns the normalized ssms\"\n",
     "    files = self.view_all_files(file_glob='*.npy')\n",
+    "    \n",
     "    for f in files:\n",
     "        f = Path(f)\n",
     "        fname = f.stem.split('_cleaned_')\n",