⏪ Bring back PyGMT VCS dependency and associated tests

Reinstate pygmt in our Pipfile (bumping it up a few versions) and enable pygmt-related tests once more. Basically reverting ea40d50 now that we've manually updated various dependencies in the Pipfile.
weiji14 · Jun 1, 2019 · b391724 · b391724
1 parent 299a032
commit b391724
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Showing 7 changed files with 58 additions and 40 deletions.
diff --git a/Pipfile b/Pipfile
@@ -19,12 +19,12 @@ numpy = "==1.16.4"
 onnx_chainer = "==1.4.1"
 optuna = "==0.11.0"
 pandas = "==0.24.2"
+pygmt = {editable = true, ref = "0.0.1a0-36-gb1e7b75", git = "https://github.com/weiji14/pygmt.git"}
 quilt = "==2.9.15"
 rasterio = "==1.0.13"
 rtree = "==0.8.3"
 scikit-image = "==0.15.0"
 tqdm = "==4.32.1"
-xarray = "==0.12.1"
 
 [dev-packages]
 behave = "==1.2.6"

diff --git a/Pipfile.lock b/Pipfile.lock
diff --git a/data_prep.ipynb b/data_prep.ipynb
@@ -28,11 +28,11 @@
      "output_type": "stream",
      "text": [
       "Python       : 3.6.6 | packaged by conda-forge | (default, Oct 11 2018, 14:33:06) \n",
-      "Geopandas    : 0.4.1\n",
-      "GMT          : 0.0.1a0+31.g97c2b4a\n",
-      "Numpy        : 1.16.2\n",
+      "Geopandas    : 0.5.0\n",
+      "GMT          : 0.0.1a0+36.gb1e7b75\n",
+      "Numpy        : 1.16.4\n",
       "Rasterio     : 1.0.13\n",
-      "Scikit-image : 0.14.2\n",
+      "Scikit-image : 0.15.0\n",
       "Xarray       : 0.12.1\n"
      ]
     }
@@ -51,7 +51,7 @@
     "import zipfile\n",
     "\n",
     "import geopandas as gpd\n",
-    "# import pygmt as gmt\n",
+    "import pygmt as gmt\n",
     "import IPython.display\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -815,10 +815,10 @@
     "\n",
     "    >>> xyz_data = 1000*pd.DataFrame(np.random.RandomState(seed=42).rand(60).reshape(20, 3))\n",
     "    >>> region = get_region(xyz_data=xyz_data)\n",
-    "    >>> grid = xyz_to_grid(xyz_data=xyz_data, region=region, spacing=250)  # doctest: +SKIP\n",
-    "    >>> grid.to_array().shape  # doctest: +SKIP\n",
+    "    >>> grid = xyz_to_grid(xyz_data=xyz_data, region=region, spacing=250)\n",
+    "    >>> grid.to_array().shape\n",
     "    (1, 5, 5)\n",
-    "    >>> grid.to_array().values  # doctest: +SKIP\n",
+    "    >>> grid.to_array().values\n",
     "    array([[[403.17618 , 544.92535 , 670.7824  , 980.75055 , 961.47723 ],\n",
     "            [379.0757  , 459.26407 , 314.38297 , 377.78555 , 546.0469  ],\n",
     "            [450.67664 , 343.26    ,  88.391594, 260.10492 , 452.3337  ],\n",
@@ -1615,5 +1615,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/data_prep.py b/data_prep.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: hydrogen
 #       format_version: '1.2'
-#       jupytext_version: 1.0.3
+#       jupytext_version: 1.1.4-rc1
 #   kernelspec:
 #     display_name: deepbedmap
 #     language: python
@@ -35,7 +35,7 @@
 import zipfile
 
 import geopandas as gpd
-# import pygmt as gmt
+import pygmt as gmt
 import IPython.display
 import matplotlib.pyplot as plt
 import numpy as np
@@ -379,10 +379,10 @@ def xyz_to_grid(
 
     >>> xyz_data = 1000*pd.DataFrame(np.random.RandomState(seed=42).rand(60).reshape(20, 3))
     >>> region = get_region(xyz_data=xyz_data)
-    >>> grid = xyz_to_grid(xyz_data=xyz_data, region=region, spacing=250)  # doctest: +SKIP
-    >>> grid.to_array().shape  # doctest: +SKIP
+    >>> grid = xyz_to_grid(xyz_data=xyz_data, region=region, spacing=250)
+    >>> grid.to_array().shape
     (1, 5, 5)
-    >>> grid.to_array().values  # doctest: +SKIP
+    >>> grid.to_array().values
     array([[[403.17618 , 544.92535 , 670.7824  , 980.75055 , 961.47723 ],
             [379.0757  , 459.26407 , 314.38297 , 377.78555 , 546.0469  ],
             [450.67664 , 343.26    ,  88.391594, 260.10492 , 452.3337  ],

diff --git a/features/data_prep.feature b/features/data_prep.feature
@@ -15,7 +15,6 @@ Feature: Data preparation
     | https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv | highres/Data_20171204_02.csv | 53cef7a0d28ff92b30367514f27e888efbc32b1bda929981b371d2e00d4c671b |
     | http://ramadda.nerc-bas.ac.uk/repository/entry/get/Polar%20Data%20Centre/DOI/Rutford%20Ice%20Stream%20bed%20elevation%20DEM%20from%20radar%20data/bed_WGS84_grid.txt?entryid=synth%3A54757cbe-0b13-4385-8b31-4dfaa1dab55e%3AL2JlZF9XR1M4NF9ncmlkLnR4dA%3D%3D | highres/bed_WGS84_grid.txt | 7396e56cda5adb82cecb01f0b3e01294ed0aa6489a9629f3f7e8858ea6cb91cf |
 
-  @skip
   Scenario Outline: Grid datasets
     Given a collection of raw high resolution datasets <input_pattern>
      When we process the data through <pipeline_file>

diff --git a/features/deepbedmap.feature b/features/deepbedmap.feature
@@ -5,7 +5,6 @@ Feature: DeepBedMap
   As a scientist,
   We want a model that produces realistic images from many open datasets
 
-  @skip
   Scenario Outline: Determine high resolution bed
     Given some view of Antarctica <bounding_box>
      When we gather low and high resolution images related to that view

diff --git a/test_ipynb.ipynb b/test_ipynb.ipynb
@@ -202,6 +202,25 @@
       "    region = get_region(xyz_data=xyz_data)\n",
       "Expecting nothing\n",
       "ok\n",
+      "Trying:\n",
+      "    grid = xyz_to_grid(xyz_data=xyz_data, region=region, spacing=250)\n",
+      "Expecting nothing\n",
+      "ok\n",
+      "Trying:\n",
+      "    grid.to_array().shape\n",
+      "Expecting:\n",
+      "    (1, 5, 5)\n",
+      "ok\n",
+      "Trying:\n",
+      "    grid.to_array().values\n",
+      "Expecting:\n",
+      "    array([[[403.17618 , 544.92535 , 670.7824  , 980.75055 , 961.47723 ],\n",
+      "            [379.0757  , 459.26407 , 314.38297 , 377.78555 , 546.0469  ],\n",
+      "            [450.67664 , 343.26    ,  88.391594, 260.10492 , 452.3337  ],\n",
+      "            [586.09906 , 469.74008 , 216.8168  , 486.9802  , 642.2116  ],\n",
+      "            [451.4794  , 652.7244  , 325.77896 , 879.8973  , 916.7921  ]]],\n",
+      "          dtype=float32)\n",
+      "ok\n",
       "2 items had no tests:\n",
       "    data_prep\n",
       "    data_prep.parse_datalist\n",
@@ -212,9 +231,9 @@
       "   2 tests in data_prep.get_region\n",
       "   3 tests in data_prep.get_window_bounds\n",
       "   3 tests in data_prep.selective_tile\n",
-      "   2 tests in data_prep.xyz_to_grid\n",
-      "22 tests in 9 items.\n",
-      "22 passed and 0 failed.\n",
+      "   5 tests in data_prep.xyz_to_grid\n",
+      "25 tests in 9 items.\n",
+      "25 passed and 0 failed.\n",
       "Test passed.\n"
      ]
     }
@@ -484,14 +503,13 @@
       "    When we download it to highres/bed_WGS84_grid.txt                                                                                                                                                                                                                                                        # features/steps/test_data_prep.py:13\n",
       "    Then the local file should have this 7396e56cda5adb82cecb01f0b3e01294ed0aa6489a9629f3f7e8858ea6cb91cf checksum                                                                                                                                                                                           # features/steps/test_data_prep.py:19\n",
       "\n",
-      "  @skip\n",
-      "  Scenario Outline: Grid datasets -- @1.1 ASCII text files to grid        # features/data_prep.feature:27\n",
-      "    Given a collection of raw high resolution datasets bed_WGS84_grid.txt # None\n",
-      "    When we process the data through bed_WGS84_grid.json                  # None\n",
-      "    And interpolate the xyz data table to bed_WGS84_grid.nc               # None\n",
-      "    Then a high resolution raster grid is returned                        # None\n",
+      "  Scenario Outline: Grid datasets -- @1.1 ASCII text files to grid        # features/data_prep.feature:26\n",
+      "    Given a collection of raw high resolution datasets bed_WGS84_grid.txt # features/steps/test_data_prep.py:25\n",
+      "    When we process the data through bed_WGS84_grid.json                  # features/steps/test_data_prep.py:38\n",
+      "    And interpolate the xyz data table to bed_WGS84_grid.nc               # features/steps/test_data_prep.py:45\n",
+      "    Then a high resolution raster grid is returned                        # features/steps/test_data_prep.py:54\n",
       "\n",
-      "  Scenario Outline: Tile datasets -- @1.1 Raster grids to tile                 # features/data_prep.feature:37\n",
+      "  Scenario Outline: Tile datasets -- @1.1 Raster grids to tile                 # features/data_prep.feature:36\n",
       "    Given a big highres raster grid 2010tr.nc                                  # features/steps/test_data_prep.py:60\n",
       "    And a collection of square bounding boxes \"model/train/tiles_3031.geojson\" # features/steps/test_data_prep.py:70\n",
       "    When we crop the big raster grid using those bounding boxes                # features/steps/test_data_prep.py:80\n",
@@ -548,12 +566,11 @@
       "  In order to create a great map of Antarctica's bed\n",
       "  As a scientist,\n",
       "  We want a model that produces realistic images from many open datasets\n",
-      "  @skip\n",
-      "  Scenario Outline: Determine high resolution bed -- @1.1 Bounding box views of Antarctica  # features/deepbedmap.feature:17\n",
-      "    Given some view of Antarctica -1593714.328,-164173.7848,-1575464.328,-97923.7848        # None\n",
-      "    When we gather low and high resolution images related to that view                      # None\n",
-      "    And pass those images into our trained neural network model                             # None\n",
-      "    Then a four times upsampled super resolution bed elevation map is returned              # None\n",
+      "  Scenario Outline: Determine high resolution bed -- @1.1 Bounding box views of Antarctica  # features/deepbedmap.feature:16\n",
+      "    Given some view of Antarctica -1593714.328,-164173.7848,-1575464.328,-97923.7848        # features/steps/test_deepbedmap.py:6\n",
+      "    When we gather low and high resolution images related to that view                      # features/steps/test_deepbedmap.py:14\n",
+      "    And pass those images into our trained neural network model                             # features/steps/test_deepbedmap.py:30\n",
+      "    Then a four times upsampled super resolution bed elevation map is returned              # features/steps/test_deepbedmap.py:38\n",
       "\n"
      ]
     }