I am an absolute newbie at openweather. When I tried their examples and was scrolling down the information, the temperature of Melbourne was 297.42. I can confirm it definitely was not so my question is do I have to format the temperature somehow because it is not really conventional (as it is a 3 digit number) and I was thinking it was wrong.
{
"temp":297.42,
"feels_like":298.33,
"temp_min":295.91,
"temp_max":299.24,
}
above is only a snapshot
Temperature in Kelvin is used by default in openweather api. You need to add ?units=metric to your api call to get Celsius.
Temperature is available in Fahrenheit, Celsius and Kelvin units.
For temperature in Fahrenheit use units=imperial
For temperature in Celsius use units=metric
Temperature in Kelvin is used by default, no need to use units parameter in API call
List of all API parameters with units openweathermap.org/weather-data.
You can specify the format you want returned for the temp. standard, metric, and imperial units are available. http://api.openweathermap.org/data/2.5/weather?q=London&mode=json&units=metric
or
http://api.openweathermap.org/data/2.5/weather?q=London&mode=json&units=imperial
just pass units are parameter in api.
For conversions -
Kelvin to Fahrenheit is:
(( kelvinValue - 273.15) * 9/5) + 32
kelvin to celsius:
Just subtract 273.15.
Related
I'm configuring the internal temperature sensor of a STM32F4 MCU, and when reading the documentation, I came across some "duplicated" but divergent definitions.
Take a look in the image below:
The temperature sensor is connected to the ADC1, channel 16. When reading the ADC value inside my room, I always get values around ~920;
The values for the calibrations (read from MCU memory) are the following:
TS_CAL1 = 941
TS_CAL2 = 1199
It seems to me that calculating the final temperature using the relationship shown on Table 69 leads to different results from when using the relationship from Table 70.
Can anyone help me in this topic? What's the difference between the data of Tables 69 and 70? What is the purpose of each one? How to calculate the temperature correctly?
As Clifford explained in the comments, the information in table 69 tells you the typical behaviour of any device from this family, whereas the pointers in table 70 give you the address of the calibration data for your particular device which were measured in the factory.
If you told me that some device of this type gave a reading of 920, I would estimate the temperature as follows:
ADC voltage = 920/4096 * 3.3V = 741mV
Voltage offset from V(25C) = 741mV - 760mV = -19mV
Temperature offset from 25C = -19/2.5 = 7.6C
Temperature = 25 - 7.6 = 17.4 degrees C
For your calibrated device I would estimate the temperature like this:
Slope = (1199 - 941) / (110 - 30) = 3.225 LSB/degree
ADC offset from ADC(30C) = (920 - 941) = -21 LSB
Temperature offset from 30C = -21 / 3.225 = 17.775 C
Temperature = 30 - 17.775 = 12.2 degrees C
It is important to note however that although this second number is "calibrated", it is done so using calibration data from much higher temperatures. To use it below 30 degrees requires to extrapolate in a way which may not be physically valid.
Ask yourself, was the room closer to 17 degrees or 12 degrees? Bear in mind that the internal temperature sensor is probably subject to a certain amount of self-heating from the high performance processor.
If you want to use the internal temperature sensor to measure low temperatures outside the calibration range like this it might be appropriate to use the offset from the lower calibration point, but then use the typical slope from the datasheet.
Note also that many STM32 evaluation boards run at 3.0V not 3.3V, so all the calculation will have to be changed if that is the case.
I can not see the table but in theory there are following points need to considers when working with sensor:
Offset: value that is different with the actual value. You may check by feed a constant voltage to system and compare with ADC value measured.
Error of sensor: you might need to measure at known value. For example, for temperature, it is used to measure at 0 temperature which is a steady state.
I want to convert Dutch RD coordinates to longitude and latitude decimal degree coordinates. Example:
108519 438598
108518 438578
108517 438578
to
51.93391 4.71134
51.93382 4.71133
51.93373 4.71131
Which packages and what code can I use to apply this on a bigger dataset?
For coordinate conversion one usually uses the proj.4 lib.
Its available for many programming languages, like python, java, c
First you need to find out the projection number as EPSG number.
e.g https://epsg.io/28992
On that page under "export" there is a section for the proj.4 definition of that projection, which gives this string:
"+proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +towgs84=565.417,50.3319,465.552,-0.398957,0.343988,-1.8774,4.0725 +units=m +no_defs"
Using the proj4 lib, you can then convert to WGS84 latitude and longitude, this is the format you want.
I have to implement kalman filtering to filter latitude longitude(from a gps sensor) to get precise position information.
i would be getting distance traveled from a wheel sensor and angle from magnetometer or i can also use gps course from gps sensor. I have latitude longitude in degrees or radians and distance in meters.
So before applying the kalman filtering i need to convert this lat/lon to meters, correct?
With this information i think i can predict using the following equation
x=x+dt*xVelocity;
y=y+dt*yVelocity;
I can calculate velocity using the following formula
xVelocity=distance*cos(angle);
yvelocity=distance*sin(angle);
So the problem here is the conversion. i tried to convert the above lat/lon to UTM and then performed the above calculation and just for testing converted it back to lat and lon to give the desired location but the results seemed wrong
For example-
double latitude=24.55662435;
double longitude=55.3525234;
double north,east;
latLonToUTM(latitude,longitude,&north,&east);
int distance=5;//5 meters
int course=200
double xVel=distance*cos(course);
double yVel=distance*sin(course);
north+=yVel;
east+=xVel;
double nxtLat,nxtLon;
UTMtoLatLon(north,east,&nxtLat,&nxtLon);
double distance=calculateDistace2LatLon(latitude,longitude,nxtLat,nxtLon);// Used online tool to get this distance
double bearing=calculateAnglebetween2LatLon(latitude,longitude,nxtLat,nxtLon);//used online tool to get angle also
here the obtained distance is not 5m and angle is 200..
Since this basic test itself is failing i am yet to go to kalman filtering.
First this conversion should be precise to even go further.
Can someone guide me to which method to use to convert this lat/lon to meters to apply velocity to get nxt meter or location?
Also if there is no need to convert then how can we add the distance travelled which is in meters to this lat lon?
How to calculate Altitude from GPS Latitude and Longitude values.What is the exact mathematical equation to solve this problem.
It is possible for a given lat,lon to determine the height of the ground (above sea level, or above Referenz Elipsoid).
But since the earth surface, mountains, etc, do not follow a mathematic formula,
there are Laser scans, performed by Satelites, that measured such a height for e.g every 30 meters.
So there exist files where you can lookup such a height.
This is called a Digital Elevation Modell, or short (DEM)
Read more here: https://en.wikipedia.org/wiki/Digital_elevation_model
Such files are huge, very few application use that approach.
Many just take the altidude value as delivered by the GPS receiver.
You can find some charts with altitude data, like Maptech's. Each pixel has a corresponding lat, long, alt/depth information.
As #AlexWien said these files are huge and most of them must be bought.
If you are interest of using these files I can help you with a C++ code to read them.
You can calculate the geocentric radius, i.e., the radius of the reference Ellipsoid which is used as basis for the GPS altitude. It can be calculated from the the GPS latitude with this formula:
Read more about this at Wikipedia.
Could someone explain how one arrives at the equation below for high pass filtering of the accelerometer values? I don't need mathematical derivation, just an intuitive interpretation of it is enough.
#define kFilteringFactor 0.1
UIAccelerationValue rollingX, rollingY, rollingZ;
- (void)accelerometer:(UIAccelerometer *)accelerometer didAccelerate:(UIAcceleration *)acceleration {
// Subtract the low-pass value from the current value to get a simplified high-pass filter
rollingX = (acceleration.x * kFilteringFactor) + (rollingX * (1.0 - kFilteringFactor));
rollingY = (acceleration.y * kFilteringFactor) + (rollingY * (1.0 - kFilteringFactor));
rollingZ = (acceleration.z * kFilteringFactor) + (rollingZ * (1.0 - kFilteringFactor));
float accelX = acceleration.x - rollingX;
float accelY = acceleration.y - rollingY;
float accelZ = acceleration.z - rollingZ;
// Use the acceleration data.
}
While the other answers are correct, here is an simplistic explanation. With kFilteringFactor 0.1 you are taking 10% of the current value and adding 90% of the previous value. Therefore the value retains a 90% similarity to the previous value, which increases its resistance to sudden changes. This decreases noise but it also makes it less responsive to changes in the signal. To reduce noise and keep it responsive you would need non trivial filters, eg: Complementary, Kalman.
The rollingX, rollingY, rollingZ values are persistent across calls to the function. They should be initialised at some point prior to use. These "rolling" values are just low pass filtered versions of the input values (aka "moving averages") which are subtracted from the instantaneous values to give you a high pass filtered output, i.e. you are getting the current deviation from the moving average.
ADDITIONAL EXPLANATION
A moving average is just a crude low pass filter. In this case it's what is known as ARMA (auto-regressive moving average) rather than just a simple MA (moving average). In DSP terms this is a recursive (IIR) filter rather than a non-recursive (FIR) filter. Regardless of all the terminology though, yes, you can think of it as a smoothing function" - it's "smoothing out" all the high frequency energy and leaving you with a slowly varying estimate of the mean value of the signal. If you then subtract this smoothed signal from the instantaneous signal then the difference will be the content that you have filtered out, i.e. the high frequency stuff, hence you get a high pass filter. In other words: high_pass_filtered_signal = signal - smoothed_signal.
Okay, what that code is doing is calculating a low-pass signal and then substracting the current value.
Thing of a square wave that takes two values 5 and 10. In other words, it oscillates between 5 and 10. Then the low pass signal is trying to find the mean (7.5). The high-pass signal is then calculated as current value minus mean, i.e. 10 - 7.5 = 2.5, or 5 - 7.5 = -2.5.
The low-pass signal is computed by integrating over past values by adding a fraction of the current value to 90% of the past low-pass value.