From 61bdb4fef88c1e83787dbb023b51d8d200844e3a Mon Sep 17 00:00:00 2001
From: Alexander M Pickering <amp215@pitt.edu>
Date: Sat, 1 Feb 2025 02:24:13 -0600
Subject: Inital commit

---
 mod.py | 62 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 mod.py

(limited to 'mod.py')

diff --git a/mod.py b/mod.py
new file mode 100644
index 0000000..1560096
--- /dev/null
+++ b/mod.py
@@ -0,0 +1,62 @@
+import csv
+import sys
+from datetime import date, timedelta
+from google.cloud import bigquery as bq
+
+class Fetcher:
+    '''Provides batches of images'''
+    #TODO TODO - you probably want to modify this to implement data augmentation
+    def __init__(self,stockfile):
+        self.startyear = 1974
+        self.nextyear = 1975
+        self.current = date(self.startyear,12,10)
+        self.curend = date(self.nextyear,12,10)
+        self.cache = {}
+        self.stocks = None
+        self.qclient = bq.Client()
+        #Load stock data, it's small enough to keep it all in memory
+        with open(stockfile) as csvfile:
+            dialect = csv.Sniffer().sniff(csvfile.read(1024))
+            csvfile.seek(0)
+            reader = csv.reader(csvfile, dialect)
+            first = True
+            for row in reader:
+                if first:
+                    first = False
+                    continue
+                tdate = row[0]
+                tdate = int(date.replace("-",""))
+                diff = float(row[4]) - float(row[1])
+                self.stocks[tdate] = diff
+        print("Loaded " + stockfile + ".")
+
+
+    def load_next(self):
+        #Load current event data 1 year at a time
+        print("I want to get stocks[" + str(self.current) + "]")
+        start_date = date(1974, 12, 10)
+        for n in range(364):
+            delt = start_date + timedelta(n)
+            rep = str(delt).replace("-","")
+
+        #Implement a cache for mysql
+        events = []
+        stockchange = 0
+        sys.exit(0);
+        x_batch = []
+        y_batch = []
+        for i in xrange(batchsize):
+            label, files = self.examples[(self.current+i) % len(self.examples)]
+            label = label.flatten()
+            # If you are getting an error reading the image, you probably have
+            # the legacy PIL library installed instead of Pillow
+            # You need Pillow
+            channels = [ misc.imread(file_io.FileIO(f,'r')) for f in files]
+            x_batch.append(np.dstack(channels))
+            y_batch.append(label)
+
+        self.current = (self.current + batchsize) % len(self.examples)
+        return np.array(x_batch), np.array(y_batch)
+
+f = Fetcher("DOW.csv")
+f.load_next()
-- 
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