Merge branch 'development'

.gitignore

@@ -4,4 +4,7 @@
 .ipynb_checkpoints/*
 .idea
 .DS_Store
-__pycache__/
+__pycache__/
+coverage.xml
+.coverage
+htmlcov

.travis.yml

@@ -24,4 +24,5 @@ install:
   - source activate test-environment
 
 script:
-  - py.test --nbval
+  - py.test --nbval-lax -v --cov . --cov-report xml
+  - python-codacy-coverage -r coverage.xml

README.md

@@ -1,8 +1,9 @@
 # ![The image slurper](img/imageslurper.small.png)
-
+[![Build Status](https://travis-ci.com/svaberg/imageslurper.svg?branch=master)](https://travis-ci.com/svaberg/imageslurper)
+[![Codacy Coverage](https://api.codacy.com/project/badge/Coverage/35f5ec8befa64a48b299c8871e004f1e)](https://www.codacy.com/manual/svaberg/imageslurper?utm_source=github.com&utm_medium=referral&utm_content=svaberg/imageslurper&utm_campaign=Badge_Coverage)
+[![Codacy Grade](https://api.codacy.com/project/badge/Grade/35f5ec8befa64a48b299c8871e004f1e)](https://www.codacy.com/manual/svaberg/imageslurper?utm_source=github.com&utm_medium=referral&utm_content=svaberg/imageslurper&utm_campaign=Badge_Grade)
 ![GitHub release (latest by date)](https://img.shields.io/github/v/release/svaberg/imageslurper)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Build Status](https://travis-ci.com/svaberg/imageslurper.svg?branch=master)](https://travis-ci.com/svaberg/imageslurper)
 
 Are you **tired** of asking people for the data behind their published false color plots? Use the image slurper to find the underlying matrix of values from a false-color (heat map) image and its colorbar!
 

environment.yml

@@ -4,4 +4,7 @@ dependencies:
   - matplotlib 
   - pillow 
   - jupyter
-  - nbval
+  - nbval
+  - pytest-cov
+  - codacy-coverage
+

imageslurper.ipynb

@@ -16,7 +16,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%reset -f\n",
     "%run imageslurper.py\n",
     "%run rectangle_picker.py\n",
     "%run plot_updater.py"

imageslurper.py

@@ -1,6 +1,9 @@
 import datetime
 import pickle
 import time
+import logging
+log = logging.getLogger(__name__)
+
 from os.path import basename
 
 import IPython.display
@@ -12,7 +15,7 @@
 import numpy as np
 from matplotlib.colors import ListedColormap
 
-__version__ = "1.1.0"
+__version__ = "1.1.2"
 
 
 def auto_crop(image, threshold=200, show_steps=False):
@@ -88,8 +91,12 @@ def auto_scale(nearest_indices, residual_norm, colorbar_data, xlim, ylim, clim):
     vmin, vmax = clim[0], clim[1]
     scaled_image = nearest_indices / colorbar_length_pixels * (vmax - vmin) + vmin
     scaled_error = residual_norm / colorbar_length_pixels * (vmax - vmin)
-    assert not np.any(np.isnan(scaled_image)), "No NaN values expected in reconstructed image."
-    assert not np.any(np.isnan(scaled_error)), "No NaN values expected in reconstructed image error."
+
+    if np.any(np.isnan(scaled_image)):
+        log.warning("Found NaN values in reconstructed image.")
+
+    if np.any(np.isnan(scaled_error)):
+        log.warning("Found NaN values in reconstructed image error.")
 
     x = np.linspace(xlim[0], xlim[1], scaled_image.shape[1])
     y = np.linspace(ylim[0], ylim[1], scaled_image.shape[0])
@@ -104,8 +111,8 @@ def auto_hole_fill(data, error, threshold, radius=5):
     :param data: image data
     :param error: error data
     :param threshold: error threshold
-    :param radius: 
-    :return: 
+    :param radius: radius in which to take median
+    :return: filled image
     """
 
     data_padded = np.empty([i + 2 * radius for i in data.shape])
@@ -325,11 +332,12 @@ def autoslurp(file,
     colorbar_image = auto_rotate(colorbar_image)
 
     image_data = np.asarray(map_image)
-
-    assert not np.any(np.isnan(image_data)), "No NaN values expected in plot area rgb image."
+    if np.any(np.isnan(image_data)):
+        log.warning("Found NaN values in plot area rgb image.")
 
     colorbar_image_data = np.asarray(colorbar_image)
-    assert not np.any(np.isnan(colorbar_image_data)), "No NaN values expected in colorbar rgb image"
+    if np.any(np.isnan(colorbar_image_data)):
+        log.warning("Found NaN values in colorbar rgb image")
 
     colorbar_data = np.median(colorbar_image_data, axis=0)  # One pixel wide
 
@@ -338,7 +346,9 @@ def autoslurp(file,
     plt.show()
 
     nearest_indices = buffered_unmap(image_data, colorbar_data, updater=updater, norm_order=1)
-    assert (nearest_indices.shape[:2] == image_data.shape[:2])
+    if not nearest_indices.shape[:2] == image_data.shape[:2]:
+        log.error("Image shapes did not match up (this is likely fatal).")
+
 
     mapped_colors = colorbar_data[nearest_indices]
 

plot_updater.py

@@ -9,7 +9,7 @@ def plot_updater(ax=None, cmap=('viridis', 'magma'), update_freq=1):
     img = yield
 
     if ax is None:
-        fig, ax = plt.subplots(figsize=(14, 6))
+        _, ax = plt.subplots(figsize=(14, 6))
 
     img_img = ax.imshow(img, cmap=cmap[0])
     ax.set_xlabel('Pixels')

rectangle_picker.py

@@ -2,9 +2,7 @@
 from matplotlib.patches import Rectangle
 from matplotlib.widgets import RectangleSelector
 
-import PIL.Image
-
-# The default rectangles and positions here are chosen to match the default 
+# The default rectangles and positions here are chosen to match the default
 # slurp target used in imageslurper.ipynb.
 _boxes = [Rectangle((50, 50), 950, 500, facecolor='green', edgecolor='green', alpha=0.4),
           Rectangle((150, 560), 750, 16, facecolor='blue', edgecolor='blue', alpha=0.4)]
@@ -16,7 +14,7 @@
 
 def line_select_callback(eclick, erelease):
     global _user_box_count
-    'eclick and erelease are the press and release events'
+    # eclick and erelease are the press and release events
     x1, y1 = eclick.xdata, eclick.ydata
     x2, y2 = erelease.xdata, erelease.ydata
 
@@ -34,7 +32,7 @@ def line_select_callback(eclick, erelease):
 def select_rectangles(image):
     global _rectangle_selector
 
-    fig, current_ax = plt.subplots(figsize=(10, 4))
+    _, current_ax = plt.subplots(figsize=(10, 4))
     current_ax.imshow(image)
     current_ax.axis('off')
 

test_imageslurper.py

@@ -0,0 +1,34 @@
+import pytest
+import PIL
+import numpy as np
+import matplotlib.pyplot as plt
+import imageslurper
+
+@pytest.mark.parametrize("error_threshold", (None, 20))
+def test_autoslurp(error_threshold):
+    """
+    Test of the whole process.
+    """
+    plt.ion()
+
+    imageslurper.autoslurp(file="img/world-temp.jpg",
+                           map_corners=np.array([  50.,   50., 1000.,  550.]),
+                           colorbar_corners=np.array([150., 560., 900., 576.]),
+                           error_threshold=error_threshold,  # In clim units
+                           xlim = (-180, 180),
+                           ylim = (90, -90),
+                           clim = (180, 280),
+                           norm_order=2,
+                           updater=None,
+                           )
+
+
+@pytest.mark.parametrize("max_pixels", (100, 1e99))
+def test_auto_resize(max_pixels):
+    """
+    Test of auto resize as it is not part of the usual slurp.
+    """
+    imageslurper.auto_resize(
+        PIL.Image.open("img/world-temp.jpg"),
+        max_pixels=max_pixels,
+        resample=PIL.Image.NEAREST)