Merge pull request #146 from neuromatch/staging

Staging

Author: iamzoltan

tutorials/W1D3_RemoteSensing/W1D3_Tutorial5.ipynb

@@ -594,7 +594,7 @@
    },
    "outputs": [],
    "source": [
-    "# calculate 12-month rolling mean for the selected location\n",
+    "# calculate 12-month rolling mean for the selected location, add in .compute() at the end if using Google Colab if it throws an error\n",
     "grid_month = precip_anom.sel(latitude=0, longitude=0, method=\"nearest\")\n",
     "grid_rolling = grid_month.rolling(time=12, center=True).mean()"
    ]
@@ -787,7 +787,7 @@
    },
    "outputs": [],
    "source": [
-    "# calculate weighted global monthly mean\n",
+    "# calculate weighted global monthly mean, add .compute() if using Google Colab and it throws an error\n",
     "anom_weighted = precip_anom.weighted(weights)\n",
     "global_weighted_mean = anom_weighted.mean((\"latitude\", \"longitude\"))\n",
     "global_weighted_mean"
@@ -1001,7 +1001,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

tutorials/W1D3_RemoteSensing/W1D3_Tutorial8.ipynb

@@ -557,6 +557,11 @@
     "# open all the monthly data files and concatenate them along the time dimension.\n",
     "# this process will take ~ 1 minute to complete due to the number of data files.\n",
     "ds_gpcp = xr.open_mfdataset(file_ob, combine=\"nested\", concat_dim=\"time\")\n",
+    "\n",
+    "# comment for colab users only: this could toss an error message for you.\n",
+    "# you should still be able to use the dataset with this error just not print ds\n",
+    "# you can try uncommenting the following line to avoid the error\n",
+    "# ds_gpcp.attrs['history']='' # the history attribute have unique chars that cause a crash on Google colab.\n",
     "ds_gpcp"
    ]
   },
@@ -1058,7 +1063,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial5.ipynb

@@ -594,7 +594,7 @@
    },
    "outputs": [],
    "source": [
-    "# calculate 12-month rolling mean for the selected location\n",
+    "# calculate 12-month rolling mean for the selected location, add in .compute() at the end if using Google Colab if it throws an error\n",
     "grid_month = precip_anom.sel(latitude=0, longitude=0, method=\"nearest\")\n",
     "grid_rolling = grid_month.rolling(time=12, center=True).mean()"
    ]
@@ -789,7 +789,7 @@
    },
    "outputs": [],
    "source": [
-    "# calculate weighted global monthly mean\n",
+    "# calculate weighted global monthly mean, add .compute() if using Google Colab and it throws an error\n",
     "anom_weighted = precip_anom.weighted(weights)\n",
     "global_weighted_mean = anom_weighted.mean((\"latitude\", \"longitude\"))\n",
     "global_weighted_mean"
@@ -1005,7 +1005,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial8.ipynb

@@ -557,6 +557,11 @@
     "# open all the monthly data files and concatenate them along the time dimension.\n",
     "# this process will take ~ 1 minute to complete due to the number of data files.\n",
     "ds_gpcp = xr.open_mfdataset(file_ob, combine=\"nested\", concat_dim=\"time\")\n",
+    "\n",
+    "# comment for colab users only: this could toss an error message for you.\n",
+    "# you should still be able to use the dataset with this error just not print ds\n",
+    "# you can try uncommenting the following line to avoid the error\n",
+    "# ds_gpcp.attrs['history']='' # the history attribute have unique chars that cause a crash on Google colab.\n",
     "ds_gpcp"
    ]
   },
@@ -1060,7 +1065,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

tutorials/W1D3_RemoteSensing/static/W1D3_Tutorial5_Solution_b9708e8c_0.png

@@ -594,7 +594,7 @@
    },
    "outputs": [],
    "source": [
-    "# calculate 12-month rolling mean for the selected location\n",
+    "# calculate 12-month rolling mean for the selected location, add in .compute() at the end if using Google Colab if it throws an error\n",
     "grid_month = precip_anom.sel(latitude=0, longitude=0, method=\"nearest\")\n",
     "grid_rolling = grid_month.rolling(time=12, center=True).mean()"
    ]
@@ -678,7 +678,7 @@
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=774.0 height=575.0 src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W1D3_RemoteSensing/static/W1D3_Tutorial5_Solution_b9708e8c_0.png>\n",
+    "<img alt='Solution hint' align='left' width=775.0 height=575.0 src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W1D3_RemoteSensing/static/W1D3_Tutorial5_Solution_b9708e8c_0.png>\n",
     "\n"
    ]
   },
@@ -767,7 +767,7 @@
    },
    "outputs": [],
    "source": [
-    "# calculate weighted global monthly mean\n",
+    "# calculate weighted global monthly mean, add .compute() if using Google Colab and it throws an error\n",
     "anom_weighted = precip_anom.weighted(weights)\n",
     "global_weighted_mean = anom_weighted.mean((\"latitude\", \"longitude\"))\n",
     "global_weighted_mean"
@@ -959,7 +959,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,