figuring out boxplots

boxplot.py

@@ -0,0 +1,82 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+import datetime
+import numpy as np
+
+df = pd.read_csv("graph_tester.csv")
+
+cold = pd.read_excel("cold.xlsx")
+warm = pd.read_excel("warm.xlsx")
+high_co2 = pd.read_excel("high_co2.xlsx")
+low_co2 = pd.read_excel("low_co2.xlsx")
+orig_db = pd.read_csv("basic_weekly.csv")
+print(orig_db)
+co2_or_temp = int(input("1 for High CO2, 2 for Warm, 3 for Low CO2, 4 for Cold"))
+
+temp_df = pd.DataFrame()
+if co2_or_temp == 1:
+    temp_df = high_co2
+elif co2_or_temp == 2:
+    temp_df = warm
+elif co2_or_temp == 3:
+    temp_df = low_co2
+else:
+    temp_df = cold
+
+plt.figure()
+temp_df = temp_df.set_index("Room #").drop("Outside Air", errors='ignore')
+temp_df = temp_df.drop("Field House NW", errors='ignore')
+temp_df = temp_df.drop("Field House NE", errors='ignore')
+temp_df = temp_df.drop("Field House SW", errors='ignore')
+temp_df = temp_df.drop("Field House SE", errors='ignore')
+# TODO: Should we drop Field House numbers?
+temp_factor = temp_df.head()
+print(temp_factor)
+temp_factor = temp_factor.T
+i_df_list = []
+room_num_list = []
+for i in temp_factor:
+    i_df = orig_db.set_index("Room #").T[i]
+    if co2_or_temp % 2 == 0:
+        i_df_list.append(i_df.T['Temperature'])
+    else:
+        i_df_list.append(i_df.T['CO2'])
+    room_num_list.append(i)
+
+print(i_df_list)
+ax = plt.axes()
+if co2_or_temp == 1:
+    ax.set_title("CO2 in 5 rooms w/ highest CO2")
+elif co2_or_temp == 2:
+    ax.set_title("Temperature in warmest 5 rooms")
+elif co2_or_temp == 3:
+    ax.set_title("CO2 in 5 rooms w/ lowest CO2")
+else:
+    ax.set_title("Temperature in coldest 5 rooms")
+room_num_list.reverse()
+i_df_list.reverse()
+print(room_num_list)
+ax.set_yticklabels(room_num_list)
+plt.boxplot(i_df_list, vert=False)
+plt.show()
+
+#try:
+    #orig_db = orig_db[orig_db["Room #"] == room_number]
+    #print(orig_db)
+    #print(orig_db["Timestamp"])
+    #orig_db = orig_db.reset_index()
+    #first_time = orig_db["Timestamp"][0]
+    #orig_db["Edited Timestamp"] = orig_db["Timestamp"].apply(lambda x: int((datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S")).timestamp()))
+    #print(first_time)
+
+    #print("This is running")
+    #plt.figure()
+    #if co2_or_temp == 1:
+        #plt.boxplot(orig_db["CO2"])
+
+    #else:
+        #plt.boxplot(orig_db["Temperature"])
+    #plt.show()
+
+#except IndexError:
+    #print("Your room is not in the list, sorry")

mpl_test.py

@@ -17,37 +17,63 @@
 room_number = input("What room?")
 co2_or_temp = int(input("1 for CO2, 2 for Temp"))
 
-orig_db = orig_db[orig_db["Room #"] == room_number]
-print(orig_db)
-print(orig_db["Timestamp"])
-orig_db = orig_db.reset_index()
-first_time = orig_db["Timestamp"][0]
-orig_db["Edited Timestamp"] = orig_db["Timestamp"].apply(lambda x: int((datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S")).timestamp()))
-print(first_time)
-
-print("This is running")
-plt.figure()
-
-new_list = []
-int_list = []
-for i in range(6):
-    temp = datetime.datetime.strptime(first_time, "%Y-%m-%d %H:%M:%S") + datetime.timedelta(i)
-    new_list.append(datetime.datetime.strftime(temp, "%Y-%m-%d"))
-    int_list.append(int(temp.timestamp()))
-
-ax = plt.axes()
-ax.set_xticks(int_list)# the problem with this is the scale
-ax.set_xlabel("Time")
-ax.set_ylabel("CO2 (ppm)")
-if co2_or_temp == 1:
-    ax.set_title("CO2 vs. Time in room " + room_number)
-else:
-    ax.set_title("Temperature vs. Time in room " + room_number)
-ax.set_xlim(left=int_list[0], right=int_list[5])
-ax.set_xticklabels(new_list)
-if co2_or_temp == 1:
-    plt.plot(orig_db['Edited Timestamp'], orig_db["CO2"])
-else:
-    plt.plot(orig_db["Edited Timestamp"], orig_db["Temperature"])
-plt.show()
+try:
+
+    orig_db = orig_db[orig_db["Room #"] == room_number]
+    print(orig_db)
+    print(orig_db["Timestamp"])
+    orig_db = orig_db.reset_index()
+    first_time = orig_db["Timestamp"][0]
+    orig_db["Edited Timestamp"] = orig_db["Timestamp"].apply(lambda x: int((datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S")).timestamp()))
+    print(first_time)
+
+    print("This is running")
+    plt.figure()
+
+    new_list = []
+    int_list = []
+    for i in range(6):
+        temp = datetime.datetime.strptime(first_time, "%Y-%m-%d %H:%M:%S") + datetime.timedelta(i)
+        new_list.append(datetime.datetime.strftime(temp, "%Y-%m-%d"))
+        int_list.append(int(temp.timestamp()))
+
+    ax = plt.axes()
+    ax.set_xticks(int_list)
+    ax.set_xlabel("Time")
+    ax.set_ylabel("CO2 (ppm)")
+    if co2_or_temp == 1:
+        ax.set_title("CO2 vs. Time in room " + room_number)
+        ax.set_ylim(0, 2000)  # should there be y-limits here or no?
+    else:
+        ax.set_title("Temperature vs. Time in room " + room_number)
+        ax.set_ylim(0, 90)  # should there be y-limits here or no?
+    ax.set_xlim(left=int_list[0], right=int_list[5])
+    ax.set_xticklabels(new_list)
+    if co2_or_temp == 1:
+        plt.scatter(orig_db['Edited Timestamp'], orig_db["CO2"])  # line plot probably worked best but like...
+    else:
+        plt.scatter(orig_db["Edited Timestamp"], orig_db["Temperature"])
+    plt.show()
+
+except IndexError:
+    print("Your room is not in the list, sorry")
+
+# Scatter plot over time is good to see just one room
+# End goal/result = dashboard
+# !!box-and-whisker plots by room for a 1-d kind of view!!
+# sensor issues --> 1 category? same numbers over time/165 intervals with problems/low co2 probably
+# actually low/high co2/temperature
+# bar charts for different groups
+# 5 subplots with each day
+# add into Word/PDF
+
+
+# TODO: categorize sensor issues based on tells such as 165 problem intervals, the same numbers over time, and low co2
+
+# TODO: turn weekly plots above into 5 daily subplots
+
+# TODO: figure out how to add into a word doc/pdf
+
+# TODO: update co2 minimum based on outside air values
 
+# TODO: update task 0 to specify parameters