Related
{'_id': ObjectId('619f4e58b1a936e640635e97'), 'OrderDate': '01-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Bars', 'Product': 'Carrot', 'Quantity': '33', 'UnitPrice': '1.77', 'TotalPrice': 58.0, '': 'Sum of TotalPrice'}
{'_id': ObjectId('619f4e58b1a936e640635e98'), 'OrderDate': '04-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Crackers', 'Product': 'Whole Wheat', 'Quantity': '87', 'UnitPrice': '3.49', 'TotalPrice': 303.0, '': '17988.66'}
{'_id': ObjectId('619f4e58b1a936e640635e99'), 'OrderDate': '07-01-2020', 'Region': 'West', 'City': 'Los Angeles', 'Category': 'Cookies', 'Product': 'Chocolate Chip', 'Quantity': '58', 'UnitPrice': '1.87', 'TotalPrice': 108.46, '': '15336.92'}
{'_id': ObjectId('619f4e58b1a936e640635e9a'), 'OrderDate': '10-01-2020', 'Region': 'East', 'City': 'New York', 'Category': 'Cookies', 'Product': 'Chocolate Chip', 'Quantity': '82', 'UnitPrice': '1.87', 'TotalPrice': 153.34, '': '33325.58'}
{'_id': ObjectId('619f4e58b1a936e640635e9b'), 'OrderDate': '13-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Cookies', 'Product': 'Arrowroot', 'Quantity': '38', 'UnitPrice': '2.18', 'TotalPrice': 82.84}
{'_id': ObjectId('619f4e58b1a936e640635e9c'), 'OrderDate': '16-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Bars', 'Product': 'Carrot', 'Quantity': '54', 'UnitPrice': '1.77', 'TotalPrice': 95.58}
{'_id': ObjectId('619f4e58b1a936e640635e9d'), 'OrderDate': '19-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Crackers', 'Product': 'Whole Wheat', 'Quantity': '149', 'UnitPrice': '3.49', 'TotalPrice': 520.01}
{'_id': ObjectId('619f4e58b1a936e640635e9e'), 'OrderDate': '22-01-2020', 'Region': 'West', 'City': 'Los Angeles', 'Category': 'Bars', 'Product': 'Carrot', 'Quantity': '51', 'UnitPrice': '1.77', 'TotalPrice': 90.27}
w=db.sales.count_documents({"Region":"West"})
e=db.sales.count_documents({"Region":"East"})
print("Total count of orders in East region and West region are",w,"and",e)
a=db.sales.aggregate([{"$project":{"Region":1,"City":1}}])
for i in a:
print(i)
Try this instead, Refer https://mongoplayground.net/p/d4L-QQXh1Id
db.collection.aggregate([
{
"$group": {
"_id": {
"Region": "$Region",
"City": "$City"
},
"Total Amount": {
"$sum": "$TotalPrice"
},
"count": {
"$sum": 1
}
},
},
{
"$project": {
"_id": 0,
"Cities": "$_id.City",
"Total Count": "$count",
"Total Amount": "$Total Amount"
}
}
])
I have a data frame with a series of events like below:
import numpy as np
import pandas as pd
ex2 = pd.DataFrame.from_dict({'order': {266: 0, 267: 1, 268: 2, 269: 3, 270: 4, 271: 5, 739: 6, 740: 7, 741: 8, 742: 9, 743: 10, 744: 11, 1657: 12, 1658: 13, 1659: 14, 1660: 15, 1661: 16, 1662: 17, 1680: 18, 1681: 19, 1682: 20, 1683: 21, 1684: 22, 1685: 23, 1739: 24, 1740: 25, 1741: 26, 1742: 27, 1743: 28, 1744: 29, 2144: 30, 2145: 31, 2146: 32, 2147: 33, 2148: 34, 2149: 35, 2232: 36, 2233: 37, 2234: 38, 2235: 39, 2236: 40, 2237: 41, 2277: 42, 2278: 43, 2279: 44, 2280: 45, 2281: 46, 2282: 47, 2403: 48, 2404: 49, 2405: 50, 2406: 51, 2407: 52, 2408: 53, 2409: 54, 2410: 55, 2411: 56, 2412: 57, 2413: 58, 2414: 59, 2552: 60, 2553: 61, 2554: 62, 2555: 63, 2556: 64, 2557: 65, 2788: 66, 2789: 67, 2790: 68, 2791: 69, 2792: 70, 2793: 71}, 'group': {266: '0', 267: '0', 268: '0', 269: '0', 270: '0', 271: '0', 739: '0', 740: '0', 741: '0', 742: '0', 743: '0', 744: '0', 1657: '1', 1658: '1', 1659: '1', 1660: '1', 1661: '1', 1662: '1', 1680: '1', 1681: '1', 1682: '1', 1683: '1', 1684: '1', 1685: '1', 1739: '1', 1740: '1', 1741: '1', 1742: '1', 1743: '1', 1744: '1', 2144: '1', 2145: '1', 2146: '1', 2147: '1', 2148: '1', 2149: '1', 2232: '1', 2233: '1', 2234: '1', 2235: '1', 2236: '1', 2237: '1', 2277: '1', 2278: '1', 2279: '1', 2280: '1', 2281: '1', 2282: '1', 2403: '1', 2404: '1', 2405: '1', 2406: '1', 2407: '1', 2408: '1', 2409: '1', 2410: '1', 2411: '1', 2412: '1', 2413: '1', 2414: '1', 2552: '1', 2553: '1', 2554: '1', 2555: '1', 2556: '1', 2557: '1', 2788: '1', 2789: '1', 2790: '1', 2791: '1', 2792: '1', 2793: '1'}, 'id': {266: 301.0, 267: 301.0, 268: 302.0, 269: 302.0, 270: 302.0, 271: 303.0, 739: 304.0, 740: 304.0, 741: 304.0, 742: 305.0, 743: 306.0, 744: 306.0, 1657: 307.0, 1658: 301.0, 1659: 308.0, 1660: 308.0, 1661: np.nan, 1662: np.nan, 1680: 310.0, 1681: 301.0, 1682: 311.0, 1683: 311.0, 1684: 311.0, 1685: 312.0, 1739: 313.0, 1740: 313.0, 1741: 313.0, 1742: 313.0, 1743: 305.0, 1744: 305.0, 2144: 301.0, 2145: 301.0, 2146: 314.0, 2147: 305.0, 2148: 305.0, 2149: 305.0, 2232: 303.0, 2233: 303.0, 2234: 315.0, 2235: np.nan, 2236: np.nan, 2237: 316.0, 2277: 301.0, 2278: 301.0, 2279: 301.0, 2280: 305.0, 2281: 305.0, 2282: 317.0, 2403: 316.0, 2404: 316.0, 2405: 304.0, 2406: 304.0, 2407: 305.0, 2408: 310.0, 2409: 308.0, 2410: 318.0, 2411: 318.0, 2412: 305.0, 2413: 305.0, 2414: 319.0, 2552: 319.0, 2553: 320.0, 2554: 320.0, 2555: 320.0, 2556: np.nan, 2557: 310.0, 2788: 302.0, 2789: 321.0, 2790: 302.0, 2791: 302.0, 2792: 315.0, 2793: 322.0}, 'type': {266: 'A', 267: 'B', 268: 'C', 269: 'D', 270: 'E', 271: 'F', 739: 'A', 740: 'A', 741: 'C', 742: 'G', 743: 'A', 744: 'H', 1657: 'I', 1658: 'J', 1659: 'C', 1660: 'D', 1661: 'K', 1662: 'F', 1680: 'L', 1681: 'B', 1682: 'C', 1683: 'D', 1684: 'E', 1685: 'F', 1739: 'A', 1740: 'A', 1741: 'C', 1742: 'D', 1743: 'K', 1744: 'F', 2144: 'A', 2145: 'F', 2146: 'C', 2147: 'M', 2148: 'N', 2149: 'F', 2232: 'A', 2233: 'I', 2234: 'C', 2235: 'K', 2236: 'F', 2237: 'O', 2277: 'O', 2278: 'A', 2279: 'C', 2280: 'P', 2281: 'F', 2282: 'O', 2403: 'O', 2404: 'I', 2405: 'C', 2406: 'D', 2407: 'P', 2408: 'Q', 2409: 'R', 2410: 'O', 2411: 'C', 2412: 'P', 2413: 'F', 2414: 'F', 2552: 'R', 2553: 'O', 2554: 'C', 2555: 'D', 2556: 'S', 2557: 'L', 2788: 'C', 2789: 'B', 2790: 'C', 2791: 'E', 2792: 'F', 2793: 'O'}, 'is_active': {266: True, 267: True, 268: True, 269: False, 270: False, 271: True, 739: True, 740: True, 741: True, 742: True, 743: True, 744: True, 1657: True, 1658: True, 1659: True, 1660: False, 1661: True, 1662: True, 1680: False, 1681: True, 1682: True, 1683: False, 1684: False, 1685: True, 1739: True, 1740: True, 1741: True, 1742: False, 1743: True, 1744: True, 2144: True, 2145: True, 2146: True, 2147: True, 2148: True, 2149: True, 2232: True, 2233: True, 2234: True, 2235: True, 2236: True, 2237: True, 2277: True, 2278: True, 2279: True, 2280: True, 2281: True, 2282: True, 2403: True, 2404: True, 2405: True, 2406: False, 2407: True, 2408: False, 2409: True, 2410: True, 2411: True, 2412: True, 2413: True, 2414: True, 2552: True, 2553: True, 2554: True, 2555: False, 2556: True, 2557: False, 2788: True, 2789: True, 2790: True, 2791: False, 2792: True, 2793: True}, 'prev_event_type': {266: np.nan, 267: 'A', 268: np.nan, 269: 'C', 270: 'C', 271: np.nan, 739: np.nan, 740: 'A', 741: 'A', 742: np.nan, 743: np.nan, 744: 'A', 1657: np.nan, 1658: np.nan, 1659: np.nan, 1660: 'C', 1661: np.nan, 1662: np.nan, 1680: np.nan, 1681: 'J', 1682: np.nan, 1683: 'C', 1684: 'C', 1685: np.nan, 1739: np.nan, 1740: 'A', 1741: 'A', 1742: 'C', 1743: np.nan, 1744: 'K', 2144: 'B', 2145: 'A', 2146: np.nan, 2147: 'F', 2148: 'M', 2149: 'N', 2232: np.nan, 2233: 'A', 2234: np.nan, 2235: np.nan, 2236: np.nan, 2237: np.nan, 2277: 'F', 2278: 'O', 2279: 'A', 2280: 'F', 2281: 'P', 2282: np.nan, 2403: 'O', 2404: 'O', 2405: np.nan, 2406: 'C', 2407: 'F', 2408: np.nan, 2409: 'C', 2410: np.nan, 2411: 'O', 2412: 'P', 2413: 'P', 2414: np.nan, 2552: 'F', 2553: np.nan, 2554: 'O', 2555: 'C', 2556: np.nan, 2557: np.nan, 2788: 'C', 2789: np.nan, 2790: np.nan, 2791: 'C', 2792: 'C', 2793: np.nan}, 'is_of_interest': {266: False, 267: False, 268: False, 269: False, 270: True, 271: False, 739: False, 740: False, 741: False, 742: False, 743: False, 744: False, 1657: False, 1658: False, 1659: False, 1660: False, 1661: False, 1662: False, 1680: False, 1681: False, 1682: False, 1683: False, 1684: True, 1685: False, 1739: False, 1740: False, 1741: False, 1742: False, 1743: False, 1744: False, 2144: False, 2145: False, 2146: False, 2147: False, 2148: False, 2149: False, 2232: False, 2233: False, 2234: False, 2235: False, 2236: False, 2237: False, 2277: False, 2278: False, 2279: False, 2280: False, 2281: False, 2282: False, 2403: False, 2404: False, 2405: False, 2406: False, 2407: False, 2408: False, 2409: False, 2410: False, 2411: False, 2412: False, 2413: False, 2414: False, 2552: False, 2553: False, 2554: False, 2555: False, 2556: False, 2557: False, 2788: False, 2789: False, 2790: False, 2791: True, 2792: False, 2793: False}})
I need to define if an event of type == 'C' results in an event of type == 'E' for particular group and id combination (so all operations will pe performed on ex2.groupby(['group', 'id']). I know that event of type == 'C' results in event of type == 'E' if there is no active (is_active == 1) event between C and E. I currently have a backward information about previous active event type contained in prev_active_type column. So for example:
for 5th row (order == 4), which is of type E I know that prev_active_type == 'C' - therefore, C led to E.
on the other hand, for 9th row (order == 8), C doesn't result in E.
What I need to obtain is a column that would tell me if event of type == 'C' resulted in event of type E (new_column == True) or not (new_column == False). I think creating an is_of_interest column which indicates if type == E is a good start, but I can't figure out how to achieve my goal from that. THe issue is that there is a variable number of events between C and E - sometimes there is none, sometimes there is one, two or event three...
The expected output is:
ex2['expected_output2'] = {266: False, 267: False, 268: True, 269: False, 270: False, 271: False, 739: False, 740: False, 741: False, 742: False, 743: False, 744: False, 1657: False, 1658: False, 1659: False, 1660: False, 1661: False, 1662: False, 1680: False, 1681: False, 1682: True, 1683: False, 1684: False, 1685: False, 1739: False, 1740: False, 1741: False, 1742: False, 1743: False, 1744: False, 2144: False, 2145: False, 2146: False, 2147: False, 2148: False, 2149: False, 2232: False, 2233: False, 2234: False, 2235: False, 2236: False, 2237: False, 2277: False, 2278: False, 2279: False, 2280: False, 2281: False, 2282: False, 2403: False, 2404: False, 2405: False, 2406: False, 2407: False, 2408: False, 2409: False, 2410: False, 2411: False, 2412: False, 2413: False, 2414: False, 2552: False, 2553: False, 2554: False, 2555: False, 2556: False, 2557: False, 2788: False, 2789: False, 2790: True, 2791: False, 2792: False, 2793: False}.values()
I try to give a small overview of the general idea first, below you find a working code example. I assume that your data is in correct order.
The following happens:
Give every entry a 'connection number', where a new connection starts at every occurance of a C row as well as after each E row.
Then, for each of these connections, check if the following conditions apply:
The connection must have one row with a C and one row with an E, because of how the connections are created in the first step, C and E rows will only happen in the first and last row respectively.
All rows between the first and last row must have 'is_active' == False
Here a working example:
# I removed your rows as they are not needed
ex2.drop(['prev_event_type', 'is_of_interest'], axis=1, inplace=True)
def is_valid_connection(connection):
is_valid = (
(connection['type'] == 'C').any() and
(connection['type'] == 'E').any() and
(~connection.iloc[1:-1]['is_active']).all()
)
return pd.Series({'result': is_valid})
ex2['connection_number'] = ((ex2['type'] == 'C') | (ex2['type'] == 'E').shift(1, fill_value=False)).cumsum()
valid_connection_ids = ex2.groupby(['group', 'id', 'connection_number']).apply(is_valid_connection)
ex2 = ex2.merge(valid_connection_ids, left_on=['group', 'id', 'connection_number'], right_index=True)
ex2.loc[ex2['type'] != 'C', 'result'] = False
ex2 = ex2.drop('connection_number', axis=1)
Here's my interpretation for the solution to the problem above:
Sort Dataframe (by order or group,id)
ex2.sort_values(by=['group', 'id'], inplace=True)
Group Dataframe by previous event type:
grouped_df = ex2.groupby(['group', 'id', 'prev_event_type']).aggregate({
'group': 'first',
'id': 'first',
'prev_event_type': 'first',
'type': lambda x: list(x),
'is_active': lambda x: list(x)
})
Iterate through zipped values to find the sequence of E (can be improved):
def check_next(nexts, actives):
check = False
for n, a in zip(nexts, actives):
if n != 'E':
check = a
else:
return not check
return False
grouped_df['output'] = grouped_df.apply(lambda x: check_next(x['type'], x['is_active']) if x['prev_event_type'] == 'C' else False, axis=1)
And now you have every group, id pair labelled where an E event occurred before C, such that there are no active-events in between.
Let me know if this helps!
I am using Ruptela Device for GPS tracking. It is working fine and i am getting data from device to server in form of hexadecimal as follows:
03d30003106796f910bb01011659ae6f4100002e0f9f6711028ac208db00000500001307030500b0000200021d35bb160010
014d000000000059ae6f4200002e0f9f6711028ac208db00000500001305030501b0000200021d35c6160016014d000000000059ae6f4a00002e0f9f6711028ac208db0000050000130703
0501b0000200021d35b7160010014d000000000059ae6f5400002e0f9f6711028ac208db00000500001307030501b0000200021d35be160010014d000000000059ae6f5e00002e0f9f7811
028ac208de00000500001307030501b0000200021d3562160010014d000000000059ae6f6800002e0fa08211028a9008b5805c0500001307030501b0010200021d3603160010014d000000
020059ae6f7200002e0fa01e11028ad308d3805c0500001307030501b0000200021d35df16000f014d000000000059ae6f7c00002e0f9ffd11028bcd08d3805c0500001307030501b00002
00021d35e9160010014d000000000059ae6f8600002e0f9f6711028b5808ca600e0600000c07030501b0010200021d35a8160010014d000000000059ae6f9000002e0f9e8e11028a1c08bc
60c20600000c07030501b0000200021d35b1160010014d000000010059ae6f9a00002e0f9e3b110289ea08c4639c0600000c07030501b0000200021d3577160010014d000000010059ae6f
a400002e0f9dc6110289b808bc58ac0600021307030501b0000200021d3597160010014d000000000059ae6fae00002e0f9dd7110289fa08a9541a0700000b07030501b0000200021d35ba
160010014d000000000059ae6fb800002e0f9db611028a2c08a07ddc0700000b07030501b0000200021d35b1160010014d000000000059ae6fc200002e0f9d7311028a80089f533e070000
0b07030501b0000200021d35bd160010014d000000020059ae6fcc00002e0f9c9a11028986089162e80600001307030501b0000200021d35c0160010014d000000020059ae6fd600002e0f
9c791102888c088762e80700000b07030501b0000200021d35c5160010014d000000000059ae6fe000002e0f9cbc110288f0088d62e80700000b07030501b0000200021d353b16000e014d
000000000059ae6fea00002e0f9cee11028954088c62e80700000b07030501b0000200021d3591160010014d000000000059ae6ff400002e0f9cbc11028975088b62e80700000b07030501
b0000200021d35bc160010014d000000000059ae6ffe00002e0f9c4711028996088d62e80700000b07030501b0000200021d35ad16000d014d000000000059ae700800002e0f9cfe11028a
5e088d62e80800000b07030501b0000200021d35a4160010014d0000000000eb34
Now the main issue is How to decode this data,
Is there some docs to decode the Data into human readable form.
I hope I am not too late with the answer.
Recently, I've developed a Node.js module that will translate this data of yours into a readable JS object. You can install the package via npm, using the following command:
npm i --save ruptela
For more details, see the package: https://www.npmjs.com/package/ruptela
Here is your data as JS object:
{
data: {
packet_length: 979,
imei: 862462030713019,
command_id: 1,
payload: {
records_left: 1,
records_total: 22,
records: [{
timestamp: 1504603969,
timestamp_extension: 0,
priority: 0,
longitude: 772775783,
latitude: 285379266,
altitude: 2267,
angle: 0,
satellites: 5,
speed: 0,
hdop: 19,
event_id: 7,
io: {
'2': 0,
'5': 0,
'22': 16,
'29': 13755,
'77': 0,
'176': 0
}
},
{
timestamp: 1504603970,
timestamp_extension: 0,
priority: 0,
longitude: 772775783,
latitude: 285379266,
altitude: 2267,
angle: 0,
satellites: 5,
speed: 0,
hdop: 19,
event_id: 5,
io: {
'2': 0,
'5': 1,
'22': 22,
'29': 13766,
'77': 0,
'176': 0
}
},
{
timestamp: 1504603978,
timestamp_extension: 0,
priority: 0,
longitude: 772775783,
latitude: 285379266,
altitude: 2267,
angle: 0,
satellites: 5,
speed: 0,
hdop: 19,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13751,
'77': 0,
'176': 0
}
},
{
timestamp: 1504603988,
timestamp_extension: 0,
priority: 0,
longitude: 772775783,
latitude: 285379266,
altitude: 2267,
angle: 0,
satellites: 5,
speed: 0,
hdop: 19,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13758,
'77': 0,
'176': 0
}
},
{
timestamp: 1504603998,
timestamp_extension: 0,
priority: 0,
longitude: 772775800,
latitude: 285379266,
altitude: 2270,
angle: 0,
satellites: 5,
speed: 0,
hdop: 19,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13666,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604008,
timestamp_extension: 0,
priority: 0,
longitude: 772776066,
latitude: 285379216,
altitude: 2229,
angle: 32860,
satellites: 5,
speed: 0,
hdop: 19,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13827,
'77': 2,
'176': 1
}
},
{
timestamp: 1504604018,
timestamp_extension: 0,
priority: 0,
longitude: 772775966,
latitude: 285379283,
altitude: 2259,
angle: 32860,
satellites: 5,
speed: 0,
hdop: 19,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 15,
'29': 13791,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604028,
timestamp_extension: 0,
priority: 0,
longitude: 772775933,
latitude: 285379533,
altitude: 2259,
angle: 32860,
satellites: 5,
speed: 0,
hdop: 19,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13801,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604038,
timestamp_extension: 0,
priority: 0,
longitude: 772775783,
latitude: 285379416,
altitude: 2250,
angle: 24590,
satellites: 6,
speed: 0,
hdop: 12,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13736,
'77': 0,
'176': 1
}
},
{
timestamp: 1504604048,
timestamp_extension: 0,
priority: 0,
longitude: 772775566,
latitude: 285379100,
altitude: 2236,
angle: 24770,
satellites: 6,
speed: 0,
hdop: 12,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13745,
'77': 1,
'176': 0
}
},
{
timestamp: 1504604058,
timestamp_extension: 0,
priority: 0,
longitude: 772775483,
latitude: 285379050,
altitude: 2244,
angle: 25500,
satellites: 6,
speed: 0,
hdop: 12,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13687,
'77': 1,
'176': 0
}
},
{
timestamp: 1504604068,
timestamp_extension: 0,
priority: 0,
longitude: 772775366,
latitude: 285379000,
altitude: 2236,
angle: 22700,
satellites: 6,
speed: 2,
hdop: 19,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13719,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604078,
timestamp_extension: 0,
priority: 0,
longitude: 772775383,
latitude: 285379066,
altitude: 2217,
angle: 21530,
satellites: 7,
speed: 0,
hdop: 11,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13754,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604088,
timestamp_extension: 0,
priority: 0,
longitude: 772775350,
latitude: 285379116,
altitude: 2208,
angle: 32220,
satellites: 7,
speed: 0,
hdop: 11,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13745,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604098,
timestamp_extension: 0,
priority: 0,
longitude: 772775283,
latitude: 285379200,
altitude: 2207,
angle: 21310,
satellites: 7,
speed: 0,
hdop: 11,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13757,
'77': 2,
'176': 0
}
},
{
timestamp: 1504604108,
timestamp_extension: 0,
priority: 0,
longitude: 772775066,
latitude: 285378950,
altitude: 2193,
angle: 25320,
satellites: 6,
speed: 0,
hdop: 19,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13760,
'77': 2,
'176': 0
}
},
{
timestamp: 1504604118,
timestamp_extension: 0,
priority: 0,
longitude: 772775033,
latitude: 285378700,
altitude: 2183,
angle: 25320,
satellites: 7,
speed: 0,
hdop: 11,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13765,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604128,
timestamp_extension: 0,
priority: 0,
longitude: 772775100,
latitude: 285378800,
altitude: 2189,
angle: 25320,
satellites: 7,
speed: 0,
hdop: 11,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 14,
'29': 13627,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604138,
timestamp_extension: 0,
priority: 0,
longitude: 772775150,
latitude: 285378900,
altitude: 2188,
angle: 25320,
satellites: 7,
speed: 0,
hdop: 11,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13713,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604148,
timestamp_extension: 0,
priority: 0,
longitude: 772775100,
latitude: 285378933,
altitude: 2187,
angle: 25320,
satellites: 7,
speed: 0,
hdop: 11,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13756,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604158,
timestamp_extension: 0,
priority: 0,
longitude: 772774983,
latitude: 285378966,
altitude: 2189,
angle: 25320,
satellites: 7,
speed: 0,
hdop: 11,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 13,
'29': 13741,
'77': 0,
'176': 0
}
},
{
timestamp: 1504604168,
timestamp_extension: 0,
priority: 0,
longitude: 772775166,
latitude: 285379166,
altitude: 2189,
angle: 25320,
satellites: 8,
speed: 0,
hdop: 11,
event_id: 7,
io: {
'2': 0,
'5': 1,
'22': 16,
'29': 13732,
'77': 0,
'176': 0
}
}
]
},
crc: 60212
},
ack: < Buffer 00 02 64 01 13 bc >
}
I am attempting to follow this example for creating a pie chart but my chart does not display labels for the sections. I copied and used the exact code from the example linked above.
The chart display complete with color sections but there are no labels like in the example.
My code is pasted below:
Ext.define('RevivalTimes.view.Chart', {
extend: 'Ext.chart.PolarChart',
xtype: 'chart',
requires: [
'Ext.chart.series.Pie',
'Ext.chart.interactions.Rotate'
],
config: {
title: 'Statistics',
iconCls: 'settings',
layout: 'fit',
animate: true,
interactions: ['rotate'],
colors: ['#115fa6', '#94ae0a', '#a61120', '#ff8809', '#ffd13e'],
store: {
fields: ['name', 'data1', 'data2', 'data3', 'data4', 'data5'],
data: [
{name: 'metric one', data1: 10, data2: 12, data3: 14, data4: 8, data5: 13},
{name: 'metric two', data1: 7, data2: 8, data3: 16, data4: 10, data5: 3},
{name: 'metric three', data1: 5, data2: 2, data3: 14, data4: 12, data5: 7},
{name: 'metric four', data1: 2, data2: 14, data3: 6, data4: 1, data5: 23},
{name: 'metric five', data1: 27, data2: 38, data3: 36, data4: 13, data5: 33}
]
},
series: [{
type: 'pie',
label: {
field: 'name',
display: 'rotate'
},
xField: 'data3',
donut: 30
}]
} //config
});
The labels show with the inclusion of labelField as shown in the code below:
series: [{
type: 'pie',
label: {
field: 'name',
display: 'rotate'
},
xField: 'data1',
donut: 30,
labelField: 'name',
showInLegend: true,
}],
I am using Sencha Touch 1.1 and have run into an aggravating little bug in my UI for which I've not been able to find the correct way to address in Sencha.
The symptom is that in my search panel containing two lists, it seems that the second list is folding directly under the first list, so in my example case the first list being one row is covering up the first row of the second list.
This is a fairly long code snippet, sorry if I got a little out of control. I just wanted to provide my exact test case (minus the sencha base code).
I've watered the app down to provide this example.
If you take the time to load it you will find that if you go into the "Job Export Search" option and enter 2 into the job order field and click "Check It!" the results will show.
I am putting a little job order info in the first list that returns one row. the second list containing many rows has export history details. Pull down on that second list and you see the first row has been tucked behind the first list.
moving doLayout() around the app has done nothing and the layout "fit" config option seems work fine except for the ui relationship between these two lists. I am assuming the ui just doesn't know the first list is taking up any space at render, but am unsure.
I'll just go ahead and say thanks for your time here at the top, you might not make it to the bottom.
rich
My Models:
Ext.regModel('JobModel', {
idProperty: 'id'
,fields: [
{type: 'int', name: 'id', field: 'id'}
,{type: 'string', name: 'market', field: 'market'}
,{type: 'string', name: 'client', field: 'client'}
,{type: 'string', name: 'title', field: 'title'}
,{type: 'string', name: 'owner', field: 'owner'}
]
});
Ext.regModel('ExportHistoryModel', {
idProperty: 'id'
,fields: [
{type: 'int', name: 'id', field: 'id'}
,{type: 'string', name: 'date_exported', field: 'date_exported'}
,{type: 'int', name: 'rank', field: 'rank'}
,{type: 'string', name: 'careersite', field: 'careersite'}
,{type: 'int', name: 'job_id', field: 'job_id'}
]
});
Ext.regModel('MenuModel', {
idProperty: 'id',
fields: [
{ name: 'id', type: 'int' }
,{ name: 'title', type: 'string' }
,{ name: 'target', type: 'string' }
]
});
My Stores:
Ext.regStore('JobStore', {
model: 'JobModel'
,sorters: [{
property: 'id',
direction: 'ASC'
}]
,proxy: {
type: 'localstorage',
id: 'adsel-app-jobstore'
}
,data: [
{id: 1, market: 'Boston', client: 'Creme Co.', title: 'Baker', owner: 'rwheadon'}
,{id: 2, market: 'Miami', client: 'Vice Inc.', title: 'Administrative Asst.', owner: 'rwheadon'}
,{id: 3, market: 'Dallas', client: 'Cowboy LLC', title: 'Customer Service', owner: 'rwheadon'}
,{id: 4, market: 'Chicago', client: 'Family Care DDM', title: 'Hygenist', owner: 'rwheadon'}
,{id: 6, market: 'Fargo', client: 'Brokers Ltd.', title: 'Sales Associate', owner: 'rwheadon'}
,{id: 10, market: 'Boise', client: 'Tate R. Well Co.', title: 'Customer Service Representative', owner: 'rwheadon'}
,{id: 21, market: 'Miami2', client: 'Vice Inc.', title: 'Administrative Asst.', owner: 'rwheadon'}
,{id: 31, market: 'Dallas2', client: 'Cowboy LLC', title: 'Customer Service', owner: 'rwheadon'}
,{id: 41, market: 'Chicago2', client: 'Family Care DDM', title: 'Hygenist', owner: 'rwheadon'}
,{id: 61, market: 'Fargo2', client: 'Brokers Ltd.', title: 'Sales Associate', owner: 'rwheadon'}
,{id: 101, market: 'Boise2', client: 'Tate R. Well Co.', title: 'Customer Service Representative', owner: 'rwheadon'}
,{id: 22, market: 'Miami3', client: 'Vice Inc.', title: 'Administrative Asst.', owner: 'rwheadon'}
,{id: 32, market: 'Dallas3', client: 'Cowboy LLC', title: 'Customer Service', owner: 'rwheadon'}
,{id: 42, market: 'Chicago3', client: 'Family Care DDM', title: 'Hygenist', owner: 'rwheadon'}
,{id: 62, market: 'Fargo3', client: 'Brokers Ltd.', title: 'Sales Associate', owner: 'rwheadon'}
,{id: 102, market: 'Boise3', client: 'Tate R. Well Co.', title: 'Customer Service Representative', owner: 'rwheadon'}
,{id: 23, market: 'Miami4', client: 'Vice Inc.', title: 'Administrative Asst.', owner: 'rwheadon'}
,{id: 33, market: 'Dallas4', client: 'Cowboy LLC', title: 'Customer Service', owner: 'rwheadon'}
,{id: 43, market: 'Chicago4', client: 'Family Care DDM', title: 'Hygenist', owner: 'rwheadon'}
,{id: 63, market: 'Fargo4', client: 'Brokers Ltd.', title: 'Sales Associate', owner: 'rwheadon'}
,{id: 103, market: 'Boise4', client: 'Tate R. Well Co.', title: 'Customer Service Representative', owner: 'rwheadon'}
]
});
Ext.regStore('ExportHistoryStore', {
model: 'ExportHistoryModel'
,sorters: [{
property: 'rank',
direction: 'ASC'
},
{property: 'job_id', direction: 'ASC'}
,{property: 'id', direction:'ASC'}]
,proxy: {
type: 'localstorage',
id: 'adsel-app-exporthistorystore'
}
,data: [
{id: 1, date_exported: '2012-03-26 08:53:00', rank: 1, careersite: 'Monster', job_id: 1}
,{id: 2, date_exported: '2012-03-26 08:53:00', rank: 2, careersite: 'Monster', job_id: 2}
,{id: 3, date_exported: '2012-03-26 08:53:00', rank: 3, careersite: 'Dice', job_id: 1}
,{id: 4, date_exported: '2012-03-26 08:53:00', rank: 4, careersite: 'Dice', job_id: 2}
,{id: 5, date_exported: '2012-03-26 08:53:00', rank: 5, careersite: 'Dice', job_id: 3}
,{id: 6, date_exported: '2012-03-26 08:53:00', rank: 6, careersite: 'Dice', job_id: 4}
,{id: 7, date_exported: '2012-03-26 08:53:00', rank: 7, careersite: 'Monster', job_id: 3}
,{id: 8, date_exported: '2012-03-26 08:53:00', rank: 8, careersite: 'Monster', job_id: 4}
,{id: 9, date_exported: '2012-03-26 08:53:00', rank: 9, careersite: 'Monster', job_id: 5}
,{id: 10, date_exported: '2012-03-26 08:53:00', rank: 10, careersite: 'Monster', job_id: 10}
,{id: 21, date_exported: '2012-03-26 08:53:00', rank: 2, careersite: 'Monster', job_id: 2}
,{id: 31, date_exported: '2012-03-26 09:53:00', rank: 3, careersite: 'Dice', job_id: 1}
,{id: 41, date_exported: '2012-03-26 09:53:00', rank: 4, careersite: 'Dice', job_id: 2}
,{id: 51, date_exported: '2012-03-26 09:53:00', rank: 5, careersite: 'Dice', job_id: 3}
,{id: 61, date_exported: '2012-03-26 09:53:00', rank: 6, careersite: 'Dice', job_id: 4}
,{id: 71, date_exported: '2012-03-26 09:53:00', rank: 7, careersite: 'Monster', job_id: 3}
,{id: 81, date_exported: '2012-03-26 09:53:00', rank: 8, careersite: 'Monster', job_id: 4}
,{id: 91, date_exported: '2012-03-26 09:53:00', rank: 9, careersite: 'Monster', job_id: 5}
,{id: 101, date_exported: '2012-03-26 09:53:00', rank: 10, careersite: 'Monster', job_id: 10}
,{id: 22, date_exported: '2012-03-26 09:53:00', rank: 2, careersite: 'Monster', job_id: 2}
,{id: 32, date_exported: '2012-03-26 11:53:00', rank: 3, careersite: 'Dice', job_id: 1}
,{id: 42, date_exported: '2012-03-26 11:53:00', rank: 4, careersite: 'Dice', job_id: 2}
,{id: 52, date_exported: '2012-03-26 11:53:00', rank: 5, careersite: 'Dice', job_id: 3}
,{id: 62, date_exported: '2012-03-26 11:53:00', rank: 6, careersite: 'Dice', job_id: 4}
,{id: 72, date_exported: '2012-03-26 11:53:00', rank: 7, careersite: 'Monster', job_id: 3}
,{id: 82, date_exported: '2012-03-26 11:53:00', rank: 8, careersite: 'Monster', job_id: 4}
,{id: 92, date_exported: '2012-03-26 11:53:00', rank: 9, careersite: 'Monster', job_id: 5}
,{id: 102, date_exported: '2012-03-26 11:53:00', rank: 10, careersite: 'Monster', job_id: 10}
,{id: 23, date_exported: '2012-03-26 11:53:00', rank: 2, careersite: 'Monster', job_id: 2}
,{id: 33, date_exported: '2012-03-26 13:53:00', rank: 3, careersite: 'Dice', job_id: 1}
,{id: 43, date_exported: '2012-03-26 13:53:00', rank: 4, careersite: 'Dice', job_id: 2}
,{id: 53, date_exported: '2012-03-26 13:53:00', rank: 5, careersite: 'Dice', job_id: 3}
,{id: 63, date_exported: '2012-03-26 13:53:00', rank: 6, careersite: 'Dice', job_id: 4}
,{id: 73, date_exported: '2012-03-26 13:53:00', rank: 7, careersite: 'Monster', job_id: 3}
,{id: 83, date_exported: '2012-03-26 13:53:00', rank: 8, careersite: 'Monster', job_id: 4}
,{id: 93, date_exported: '2012-03-26 13:53:00', rank: 9, careersite: 'Monster', job_id: 5}
,{id: 103, date_exported: '2012-03-26 13:53:00', rank: 10, careersite: 'Monster', job_id: 10}
,{id: 24, date_exported: '2012-03-26 13:53:00', rank: 2, careersite: 'Monster', job_id: 2}
,{id: 34, date_exported: '2012-03-26 15:53:00', rank: 3, careersite: 'Dice', job_id: 1}
,{id: 44, date_exported: '2012-03-26 15:53:00', rank: 4, careersite: 'Dice', job_id: 2}
,{id: 54, date_exported: '2012-03-26 15:53:00', rank: 5, careersite: 'Dice', job_id: 3}
,{id: 64, date_exported: '2012-03-26 15:53:00', rank: 6, careersite: 'Dice', job_id: 4}
,{id: 74, date_exported: '2012-03-26 15:53:00', rank: 7, careersite: 'Monster', job_id: 3}
,{id: 84, date_exported: '2012-03-26 15:53:00', rank: 8, careersite: 'Monster', job_id: 4}
,{id: 94, date_exported: '2012-03-26 15:53:00', rank: 9, careersite: 'Monster', job_id: 5}
,{id: 104, date_exported: '2012-03-26 15:53:00', rank: 10, careersite: 'Monster', job_id: 10}
,{id: 25, date_exported: '2012-03-26 15:53:00', rank: 2, careersite: 'Monster', job_id: 2}
,{id: 35, date_exported: '2012-03-26 20:53:00', rank: 3, careersite: 'Dice', job_id: 1}
,{id: 45, date_exported: '2012-03-26 20:53:00', rank: 4, careersite: 'Dice', job_id: 2}
,{id: 55, date_exported: '2012-03-26 20:53:00', rank: 5, careersite: 'Dice', job_id: 3}
,{id: 65, date_exported: '2012-03-26 20:53:00', rank: 6, careersite: 'Dice', job_id: 4}
,{id: 75, date_exported: '2012-03-26 20:53:00', rank: 7, careersite: 'Monster', job_id: 3}
,{id: 85, date_exported: '2012-03-26 20:53:00', rank: 8, careersite: 'Monster', job_id: 4}
,{id: 95, date_exported: '2012-03-26 20:53:00', rank: 9, careersite: 'Monster', job_id: 5}
,{id: 106, date_exported: '2012-03-26 20:53:00', rank: 10, careersite: 'Monster', job_id: 10}
,{id: 1116, date_exported: '2012-03-26 08:53:00', rank: 11, careersite: 'Craigslist', job_id: 10}
,{id: 1216, date_exported: '2012-03-26 08:53:00', rank: 12, careersite: 'Craigslist', job_id: 6}
,{id: 1316, date_exported: '2012-03-26 08:53:00', rank: 13, careersite: 'Craigslist', job_id: 3}
,{id: 1416, date_exported: '2012-03-26 08:53:00', rank: 14, careersite: 'Craigslist', job_id: 1}
,{id: 1516, date_exported: '2012-03-26 08:53:00', rank: 15, careersite: 'Craigslist', job_id: 4}
,{id: 1616, date_exported: '2012-03-26 08:53:00', rank: 16, careersite: 'Monster', job_id: 6}
]
});
Ext.regStore('HomeMenuStore', {
model: 'MenuModel'
,sorters: [{
property: 'title',
direction: 'ASC'
}]
,proxy: {
type: 'localstorage',
id: 'adsel-app-homemenustore'
}
,data: [
// {id: 1, title: 'Status', targetMenu: 'statusList'}
{id:2, title:'Jobs', target: 'jobListPanel'}
,{id:3, title:'Export History', target: 'exportListPanel'}
,{id:4, title: 'Job Export Search', target: 'jobExportSearchPanel'}
]
});
overlappingListProblem.js (intitializers)
var App;
// new Ext.Application({
new Ext.Application({
name : 'AdSel',
useLoadMask : true,
launch: function (){
App = this;
//initialization
AdSel.views.homeMenuPanel = new Ext.Panel({
id: 'homeMenuPanel'
,layout: 'fit'
,dockedItems: [
AdSel.views.consoleBar
]
,items: [
AdSel.views.consoleList
]
});
AdSel.views.jobListPanel = new Ext.Panel({
id: 'jobListPanel'
,layout: 'fit'
,dockedItems: [
AdSel.views.jobsToolbar
]
,items: [
AdSel.views.jobList
]
});
AdSel.views.exportListPanel = new Ext.Panel({
id: 'exportListPanel'
,layout: 'fit'
,dockedItems: [
AdSel.views.exporthistoryToolbar
]
,items: [
AdSel.views.exporthistoryList
]
});
AdSel.views.jobExportSearchPanel = new Ext.Panel({
id: 'jobExportSearchPanel'
,layout: 'fit'
,dockedItems: [
AdSel.views.jobHistoryCheckerToolbar
,AdSel.views.joborderHistoryCheckForm
,AdSel.views.joborderPostingHistoryHeaderInfo
]
,items: [
AdSel.views.joborderPostingHistoryResultList
]
});
//render
AdSel.views.viewport = new Ext.Panel({
fullscreen : true
,layout : 'card'
,cardAnimation : 'slide'
,items: [
AdSel.views.homeMenuPanel
,AdSel.views.jobListPanel
,AdSel.views.exportListPanel
,AdSel.views.jobExportSearchPanel
]
})
}
});
hardwiredmenu-screens.js:
//MAIN SCREEN (HOME)
AdSel.views.consoleBar = new Ext.Toolbar({
id: 'consoleBar',
title: 'Job Management Console'
});
AdSel.views.consoleList = new Ext.List({
id: 'consoleList'
,store: 'HomeMenuStore'
,disableSelection: true
,itemTpl: '<div class="list-item-title">{title}</div>'
,onItemDisclosure: function (record) {
var selectedNote=record;
var theTarget = selectedNote.data.target;
if( theTarget == 'jobExportSearchPanel') {
Ext.getStore('JobStore').filter({property:'id', value: 0, exactMatch: true});
Ext.getStore('ExportHistoryStore').filter({property:'job_id', value: 0, exactMatch: true});
AdSel.views.viewport.setActiveItem(theTarget, {type: 'slide', direction: 'left'})
} else {
AdSel.views.viewport.setActiveItem(theTarget, {type: 'slide', direction: 'left'});
}
}
});
job-screens.js
//JOBS SCREEN
AdSel.views.jobsToolbar = new Ext.Toolbar({
id: 'jobsToolbar'
,ui: 'light'
,defaults: {
iconMask: true
,ui: 'plain'
}
,title: 'Jobs'
,items: [
{
iconCls: 'home',
handler: function () {
AdSel.views.viewport.setActiveItem('homeMenuPanel', {type: 'slide', direction: 'right'});
}
}
]
});
AdSel.views.jobList = new Ext.List({
id: 'jobList'
,store: 'JobStore'
,disableSelection: true
,itemTpl: '<div class="list-item-title">{title} ({client}) in {market} entered by {owner}</div>'
});
//JOB EXPORTHISTORY SEARCH SCREEN
/*
* *********************************************** *
* *********************************************** *
* *********************************************** *
* JOB HISTORY *
* *********************************************** *
* *********************************************** *
* *********************************************** *
*/
AdSel.views.jobHistoryCheckerToolbar = new Ext.Toolbar({
id: 'jobHistoryCheckerToolbar'
,ui: 'light'
,dock: 'top'
,defaults: {
iconMask: true
,ui: 'plain'
}
// ,title: 'Job Posting History'
,title: 'Job Export History'
,items: [
{
iconCls: 'home'
,id: 'jobhistorycheckerHomeBtn'
,handler: function(){
AdSel.views.joborderHistoryCheckForm.reset();
Ext.getStore('JobStore').clearFilter();
Ext.getStore('ExportHistoryStore').clearFilter();
AdSel.views.viewport.setActiveItem('homeMenuPanel', {type: 'slide', direction: 'right'});
}
}
,{xtype: 'spacer'}
,{
text: 'Check it!'
,ui: 'action'
,handler: function () {
var ovrEditor = AdSel.views.joborderHistoryCheckForm;
var filterid = ovrEditor.getValues().joborderid;
var theHeaderStore = Ext.getStore('JobStore');
theHeaderStore.clearFilter();
theHeaderStore.filter({property:'id', value: filterid, exactMatch: true});
var theHistoryStore = Ext.getStore('ExportHistoryStore');
theHistoryStore.clearFilter();
theHistoryStore.filter({property:'job_id', value: filterid, exactMatch: true});
var theHeaderList = AdSel.views.joborderPostingHistoryHeaderInfo;
theHeaderList.refresh();
var theHistoryList = AdSel.views.joborderPostingHistoryResultList;
theHistoryList.refresh();
AdSel.views.jobExportSearchPanel.doLayout();
}
}
]
});
AdSel.views.joborderHistoryCheckForm = new Ext.form.FormPanel({
id: 'joborderHistoryCheckForm'
,standardSubmit: false
,submitOnAction: false
,items: [
{
xtype: 'textfield'
,id: 'joborderhistorycheckform-joborderid'
,name: 'joborderid'
,label: 'JO Number'
}
]
});
AdSel.views.joborderPostingHistoryHeaderInfo = new Ext.List({
id: 'joborderPostingHistoryHeaderInfo'
// ,layout: 'fit'
,store: 'JobStore'
,disableSelection: true
,itemTpl: '<div class="list-item-title-dark"><b>Job Order: </b>{id}<br/>' +
'<b>Title: </b>{title}<br/><b>Client: </b>{client}</div>'
});
AdSel.views.joborderPostingHistoryResultList = new Ext.List({
id: 'joborderPostingHistoryResultList'
// ,layout: 'fit'
,store: 'ExportHistoryStore'
,disableSelection: true
,itemTpl: '<div class="list-item-title">Runtime: {date_exported}<br/>' +
'Career Site: {careersite}<br/>Rank: {rank}</div>'
});
exporthistory-screens.js
//EXPORTS SCREEN
AdSel.views.exporthistoryToolbar = new Ext.Toolbar({
id: 'jobsToolbar'
,ui: 'light'
,defaults: {
iconMask: true
,ui: 'plain'
}
,title: 'Export History'
,items: [
{
iconCls: 'home',
handler: function () {
AdSel.views.viewport.setActiveItem('homeMenuPanel', {type: 'slide', direction: 'right'});
}
}
]
});
AdSel.views.exporthistoryList = new Ext.List({
id: 'exporthistoryList'
,store: 'ExportHistoryStore'
,disableSelection: true
,itemTpl: '<div class="list-item-title">{date_exported} exported job# {job_id} to {careersite} (ranked {rank})</div>'
});
I am assuming most won't need the index.html, but I might as well burn a few more bytes:
<html>
<head>
<link rel="stylesheet" type="text/css" href="touch/1.1.1/resources/css/sencha-touch.css"/>
<script type="text/javascript" src="touch/1.1.1/sencha-touch.js"></script>
<script type="text/javascript" src="app/overlappingListProblem.js"></script>
<script type="text/javascript" src="app/models/models.js"></script>
<script type="text/javascript" src="app/models/stores.js"></script>
<script type="text/javascript" src="app/viewers/exporthistory-screens.js"></script>
<script type="text/javascript" src="app/viewers/hardwiredmenu-screens.js"></script>
<script type="text/javascript" src="app/viewers/job-screens.js"></script>
</head>
<body>
</body>
</html>
Posting my alternative approach where I use a form instead of stacking the two dynamic lists together in a panel:
The Form Panel
AdSel.views.joborderPostingHistoryHeaderInfoForm = new Ext.form.FormPanel({
id: 'joborderPostingHistoryHeaderInfoForm'
,standardSubmit: false
,submitOnAction: false
,items: [
{
xtype: 'textfield'
,id: 'joborderPostingHistoryHeaderInfoForm-adTitle'
,name: 'adTitle'
,label: 'Job Title'
,listeners: {
afterrender: function(ele) {
ele.fieldEl.dom.readOnly = true;
}
}
}
,{
xtype: 'textfield'
,id: 'joborderPostingHistoryHeaderInfoForm-clientName'
,name: 'clientName'
,label: 'Client'
,listeners: {
afterrender: function(ele) {
ele.fieldEl.dom.readOnly = true;
}
}
}
]
});
And then load the desired record into that form adding a little code to the Check It! button:
theHeaderStore = Ext.getStore('exactMatchJobStore');
theHeaderStore.removeAll();
theHeaderStore.load({
params:{id: filterid}
});
theHeaderStore.on('datachanged', function(){
var selectedNote = this.first();
AdSel.views.joborderPostingHistoryHeaderInfoForm.loadRecord(selectedNote);
});