Make plot_dates.py leverage photo exifs

datasets/rafael/240424/plot_dates.py

@@ -5,46 +5,36 @@ import numpy as np
 
 import matplotlib.dates as mdates
 
-try:
+from PIL import Image
-    # Try to fetch a list of Matplotlib releases and their dates
+from PIL.ExifTags import TAGS
-    # from https://api.github.com/repos/matplotlib/matplotlib/releases
-    import json
-    import urllib.request
 
-    url = 'https://api.github.com/repos/matplotlib/matplotlib/releases'
+import os
-    url += '?per_page=100'
-    data = json.loads(urllib.request.urlopen(url, timeout=1).read().decode())
 
-    dates = []
+os.chdir('photos')
-    names = []
-    for item in data:
-        if 'rc' not in item['tag_name'] and 'b' not in item['tag_name']:
-            dates.append(item['published_at'].split("T")[0])
-            names.append(item['tag_name'])
-    # Convert date strings (e.g. 2014-10-18) to datetime
-    dates = [datetime.strptime(d, "%Y-%m-%d") for d in dates]
 
-except Exception:
+names = []
-    # In case the above fails, e.g. because of missing internet connection
+dates = []
-    # use the following lists as fallback.
-    names = ['v2.2.4', 'v3.0.3', 'v3.0.2', 'v3.0.1', 'v3.0.0', 'v2.2.3',
-             'v2.2.2', 'v2.2.1', 'v2.2.0', 'v2.1.2', 'v2.1.1', 'v2.1.0',
-             'v2.0.2', 'v2.0.1', 'v2.0.0', 'v1.5.3', 'v1.5.2', 'v1.5.1',
-             'v1.5.0', 'v1.4.3', 'v1.4.2', 'v1.4.1', 'v1.4.0']
 
-    dates = ['2019-02-26', '2019-02-26', '2018-11-10', '2018-11-10',
+for fileName in sorted(os.listdir()):
-             '2018-09-18', '2018-08-10', '2018-03-17', '2018-03-16',
+    #print(fileName)
-             '2018-03-06', '2018-01-18', '2017-12-10', '2017-10-07',
+    if fileName.endswith('.JPG'):
-             '2017-05-10', '2017-05-02', '2017-01-17', '2016-09-09',
+        image = Image.open(fileName)
-             '2016-07-03', '2016-01-10', '2015-10-29', '2015-02-16',
+        imageExif = image.getexif()
-             '2014-10-26', '2014-10-18', '2014-08-26']
+        dateTimeKey = list(TAGS.keys())[list(TAGS.values()).index('DateTime')]
+        dateTime = imageExif[dateTimeKey]
+        #print(dateTime)
+        names += [fileName.replace('DSC0', '').replace('.JPG', '')]
+        dates += [dateTime]
+        #break
 
-    # Convert date strings (e.g. 2014-10-18) to datetime
+# Convert date strings to datetime
-    dates = [datetime.strptime(d, "%Y-%m-%d") for d in dates]
+dates = [datetime.strptime(d, "%Y:%m:%d %H:%M:%S") for d in dates]
 
 # Choose some nice levels
-levels = np.tile([-5, 5, -3, 3, -1, 1],
+NUMBER_OF_LEVELS = 10
-                 int(np.ceil(len(dates)/6)))[:len(dates)]
+actualLevels = range(-NUMBER_OF_LEVELS * 2 + 1, NUMBER_OF_LEVELS * 2 , 2)
+levels = np.tile(actualLevels,
+                 int(np.ceil(len(dates)/len(actualLevels))))[:len(dates)]
 
 # Create figure and plot a stem plot with the date
 fig, ax = plt.subplots(figsize=(8.8, 4), layout="constrained")
@@ -62,8 +52,7 @@ for d, l, r in zip(dates, levels, names):
                 verticalalignment="bottom" if l > 0 else "top")
 
 # format x-axis with 4-month intervals
-ax.xaxis.set_major_locator(mdates.MonthLocator(interval=4))
+ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y:%m:%d %H:%M:%S"))
-ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
 plt.setp(ax.get_xticklabels(), rotation=30, ha="right")
 
 # remove y-axis and spines
@@ -71,4 +60,4 @@ ax.yaxis.set_visible(False)
 ax.spines[["left", "top", "right"]].set_visible(False)
 
 ax.margins(y=0.1)
-plt.show()
+plt.show()