diff --git a/Interactive_Aggregate_Uncertainty.ipynb b/Interactive_Aggregate_Uncertainty.ipynb
index 91cb407..037f833 100644
--- a/Interactive_Aggregate_Uncertainty.ipynb
+++ b/Interactive_Aggregate_Uncertainty.ipynb
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -286,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -373,9 +373,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X extents [0.0,1000.0] and Y entents [0.0,1000.0]\n"
+     ]
+    }
+   ],
    "source": [
     "csiz = 100; xmn = csiz * 0.5; nx = 10; ymn = csiz * 0.5; ny = 10 \n",
     "xmin = xmn - csiz * 0.5; xmax = xmin + nx * csiz\n",
@@ -398,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -597,7 +605,7 @@
     "\n",
     "# plot the distribution of the simulated values   \n",
     "    plt.subplot(1,3,3)\n",
-    "    plt.hist(metric,bins = np.linspace(0,.30,60),alpha=0.2,color=\"red\",edgecolor=\"black\", normed = True)\n",
+    "    plt.hist(metric,bins = np.linspace(0,.30,60),alpha=0.2,color=\"red\",edgecolor=\"black\", density = True)\n",
     "    plt.xlim([.0,.30]); plt.ylim([0,nreal])\n",
     "    plt.title('Spatial Aggregate Uncertainty')\n",
     "    plt.xlabel('Spatial Aggregate'); plt.ylabel('Frequency')\n",
@@ -643,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 12,
    "metadata": {
     "scrolled": false
    },
@@ -651,7 +659,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a9f04b3e44db41f8ad790d46c3d7cc54",
+       "model_id": "640f35fb10244ae98a8192d07ffc4868",
        "version_major": 2,
        "version_minor": 0
       },
@@ -665,12 +673,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f59b7b4e083f49519e317023fa428979",
+       "model_id": "2804cf118e1e412580e4431d05e7f936",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 432x288 with 4 Axes>', 'i…"
+       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 4 Axes>', 'i…"
       ]
      },
      "metadata": {},
@@ -744,7 +752,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/Interactive_Spatial_Aggregate_Uncertainty.ipynb b/Interactive_Spatial_Aggregate_Uncertainty.ipynb
index cbc5094..8fc2ba5 100644
--- a/Interactive_Spatial_Aggregate_Uncertainty.ipynb
+++ b/Interactive_Spatial_Aggregate_Uncertainty.ipynb
@@ -604,7 +604,7 @@
     "\n",
     "# plot the distribution of the simulated values   \n",
     "    plt.subplot(1,3,3)\n",
-    "    plt.hist(metric,bins = np.linspace(0,.30,100),alpha=0.2,color=\"red\",edgecolor=\"black\", normed = False)\n",
+    "    plt.hist(metric,bins = np.linspace(0,.30,100),alpha=0.2,color=\"red\",edgecolor=\"black\", density = False)\n",
     "    plt.xlim([.0,.30]); plt.ylim([0,nsim/2])\n",
     "    plt.title('Spatial Aggregate Uncertainty')\n",
     "    plt.xlabel('Spatial Aggregate'); plt.ylabel('Frequency')\n",
diff --git a/Interactive_Spatial_Aggregate_Uncertainty_Pad.ipynb b/Interactive_Spatial_Aggregate_Uncertainty_Pad.ipynb
index 0de2793..2b1cf4f 100644
--- a/Interactive_Spatial_Aggregate_Uncertainty_Pad.ipynb
+++ b/Interactive_Spatial_Aggregate_Uncertainty_Pad.ipynb
@@ -599,13 +599,13 @@
     "\n",
     "# plot the distribution of the simulated values   \n",
     "    plt.subplot(1,3,3)\n",
-    "    plt.hist(metric,bins = np.linspace(0,.30,30),alpha=0.2,color=\"red\",edgecolor=\"black\", normed = True,label=\"spatial\")\n",
+    "    plt.hist(metric,bins = np.linspace(0,.30,30),alpha=0.2,color=\"red\",edgecolor=\"black\", density = True,label=\"spatial\")\n",
     "    plt.xlim([.0,.30]); plt.ylim([0,nreal])\n",
     "    plt.title('Spatial Aggregate Uncertainty')\n",
     "    plt.xlabel('Spatial Aggregate'); plt.ylabel('Frequency')\n",
     "    \n",
     "    standard_samples = np.random.normal(np.average(metric),math.sqrt(tvar / nsim_loc),1000)\n",
-    "    plt.hist(standard_samples,bins = np.linspace(0,.30,30),alpha=0.2,color=\"black\",edgecolor=\"black\",normed = True,label=\"non-spatial\")\n",
+    "    plt.hist(standard_samples,bins = np.linspace(0,.30,30),alpha=0.2,color=\"black\",edgecolor=\"black\", density = True,label=\"non-spatial\")\n",
     "    plt.legend()\n",
     "                                        \n",
     "    ax = plt.gca()\n",
diff --git a/environment.yml b/environment.yml
index 1504c48..2cbef3a 100644
--- a/environment.yml
+++ b/environment.yml
@@ -7,8 +7,10 @@ dependencies:
   - scipy
   - matplotlib
   - ipywidgets
-  - pandas
+  - pandas<2.0
+  - seaborn<0.11.0
   - scikit-learn
+  - tqdm
   - numba
   - statsmodels
   - pip