Re-run tutorials after merge

Author: jsiegle

doc_template/examples_root/examples/nb/ecephys_data_access.ipynb

@@ -16,9 +16,9 @@
     "\n",
     "## Options for data access\n",
     "\n",
-    "The recommended way of interacting with the data is via the **`EcephysProjectCache` object of the AllenSDK**. This method abstracts away the details of on-disk file storage, and delivers the data to you as ready-to-analyze Python objects. The cache will automatically keep track of which files are stored locally, and will download additional files on an as-needed basis. Usually you won't need to worry about how these files are structured, but this tutorial will cover those details in case you want to analyze them without using the AllenSDK (e.g., in Matlab). This tutorial begins with <a href='#Using-the-AllenSDK-to-retrieve-data'>an introduction to this approach</a>.\n",
+    "The **`EcephysProjectCache` object of the AllenSDK** is the easiest way to interact with the data. This object abstracts away the details of on-disk file storage, and delivers the data to you as ready-to-analyze Python objects. The cache will automatically keep track of which files are stored locally, and will download additional files on an as-needed basis. Usually you won't need to worry about how these files are structured, but this tutorial will cover those details in case you want to analyze them without using the AllenSDK (e.g., in Matlab). This tutorial begins with <a href='#Using-the-AllenSDK-to-retrieve-data'>an introduction to this approach</a>.\n",
     "\n",
-    "If you have an **Amazon Web Services (AWS)** account, all of the data is available via the Allen Brain Observatory Simple Storage Service (S3) bucket. This is an AWS Public Dataset located at `arn:aws:s3:::allen-brain-observatory` in region `us-west-2`. Launching a Jupyter notebook instance on AWS will allow you to access the complete dataset without having to download anything locally. This includes around 80 TB of raw data files, which are not accessible via the AllenSDK. The only drawback is that you'll need to pay for the time that your instance is running—but this can still be economical in many cases. A brief overview of this approach can be found <a href='#Accessing-data-on-AWS'>below</a>.\n",
+    "If you have an **Amazon Web Services (AWS)** account, you can use an `EcephysProjectCache` object to access the data via the Allen Brain Observatory Simple Storage Service (S3) bucket. This is an AWS Public Dataset located at `arn:aws:s3:::allen-brain-observatory` in region `us-west-2`. Launching a Jupyter notebook instance on AWS will allow you to access the complete dataset without having to download anything locally. This includes around 80 TB of raw data files, which are not accessible via the AllenSDK. The only drawback is that you'll need to pay for the time that your instance is running—but this can still be economical in many cases. A brief overview of this approach can be found <a href='#Accessing-data-on-AWS'>below</a>.\n",
     "\n",
     "A third option is to directly download the data via **api.brain-map.org**. This should be used only as a last resort if the other options are broken or are not available to you. Instructions for this can be found <a href='#Direct-download-via-api.brain-map.org'>at the end of this tutorial</a>."
    ]
@@ -1117,7 +1117,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you want to analyze the data without downloading anything to your local machine, you can use AWS.\n",
+    "If you want to analyze the data without downloading anything to your local machine, you can use the AllenSDK on AWS.\n",
     "\n",
     "Follow [these instructions](https://github.com/AllenInstitute/AllenSDK/wiki/Use-the-Allen-Brain-Observatory-%E2%80%93-Visual-Coding-on-AWS) to launch a Jupyter notebook. Then, simply point to the existing manifest file in the Allen Institute's S3 bucket, and all of the data will be immediately available:"
    ]

doc_template/examples_root/examples/nb/ecephys_lfp_analysis.ipynb

@@ -884,7 +884,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/joshs/anaconda3/envs/ecephys-tutorials/lib/python3.7/site-packages/allensdk/brain_observatory/ecephys/ecephys_session.py:1062: UserWarning: Session includes invalid time intervals that could be accessed with the attribute 'invalid_times',Spikes within these intervals are invalid and may need to be excluded from the analysis.\n",
+      "/home/joshs/anaconda3/envs/ecephys-tutorials/lib/python3.7/site-packages/allensdk/brain_observatory/ecephys/ecephys_session.py:1088: UserWarning: Session includes invalid time intervals that could be accessed with the attribute 'invalid_times',Spikes within these intervals are invalid and may need to be excluded from the analysis.\n",
       "  warnings.warn(\"Session includes invalid time intervals that could be accessed with the attribute 'invalid_times',\"\n"
      ]
     }

doc_template/examples_root/examples/nb/ecephys_quality_metrics.ipynb

@@ -697,7 +697,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, calculating the receptive field is as simple as calling `get_receptive_field()` with a unit ID as the input argument:"
+    "Now, calculating the receptive field is as simple as calling `_get_rf()` with a unit ID as the input argument.\n",
+    "\n",
+    "**NOTE:** We are planning to change this method to `get_receptive_field()` in a future SDK release."
    ]
   },
   {
@@ -715,7 +717,7 @@
     }
    ],
    "source": [
-    "RF = rf_mapping.get_receptive_field(v1_units.index.values[3])"
+    "RF = rf_mapping._get_rf(v1_units.index.values[3])"
    ]
   },
   {
@@ -780,7 +782,7 @@
    ],
    "source": [
     "def plot_rf(unit_id, index):\n",
-    "    RF = rf_mapping.get_receptive_field(unit_id)\n",
+    "    RF = rf_mapping._get_rf(unit_id)\n",
     "    _ = plt.subplot(6,10,index+1)\n",
     "    _ = plt.imshow(RF)\n",
     "    _ = plt.axis('off')\n",